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\received{September, 1994}\revised{July, 1995}

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\title{Acquisition of lexical translation relations from MRDs}

%% Author name 
\authors{Ann Copestake, Ted Briscoe} 
\email{aac@csli.stanford.edu} 
%% Affiliation address
\affil{University of Cambridge, 
Computer Laboratory}

%% Author name 
\author{Piek Vossen}
\email{Piek.Vossen@let.uva.nl}

%% Affiliation address
\affil{Computer Centrum Letteren, Universiteit v. Amsterdam}

%% Author name 
\author{Alicia Ageno, Irene Castellon, Francesc Ribas,}
\email{} 
\author{German Rigau, Horacio Rodr\'{i}guez, Anna Samiotou}
\email{horacio@lsi.upc.es}

%% Affiliation address
\affil{Facultad de Informatica, 
Universitat Polit\`{e}cnica de Catalunya}


%% Abstract
\abstract{In 
this paper we present a methodology for extracting information about 
lexical translation equivalences from the machine readable 
versions of conventional
dictionaries (MRDs), and describe a series of experiments on 
semi-automatic construction of a
linked multilingual lexical knowledge base for English, Dutch, and Spanish.
We discuss the advantages and limitations of using MRDs that this has 
revealed, 
and some strategies we have developed to cover gaps where
no direct translation can be found.}

%% Keywords
\keywords{Machine readable dictionaries, lexical acquisition, lexical
knowledge bases}

\section{Introduction}

The research reported here is part of the ACQUILEX~II project which has
as one aim
the automatic or semi-automatic construction of fragments of
a multi-lingual
lexical knowledge base containing detailed syntactic and semantic
information from MRD resources.  
It has been estimated that the average time needed to construct a 
lexical entry 
for a natural language processing
system manually is about 30 minutes \cite{neff}: since a lexicon 
must contain at least 40,000 words 
to achieve even moderately wide coverage \cite{walk+ams} it is
clear that techniques which either partially or totally automate this process
should be investigated.

Three major classes of techniques for lexicon acquisition have been
developed:  machine-aided manual construction, extraction from corpora
and extraction from MRDs.  Each of these techniques has advantages and
disadvantages.
Various tools have been developed which make 
manual lexicon construction
and maintenance quicker and easier, 
and to some extent avoid the need for detailed linguistic
knowledge.  This approach has been taken in large-scale MT systems, see for
example, \cite{nir+rask} and
the description of METAL in \cite{hutch+som}.  
Manual construction is the most reliable technique,
but it is time-consuming
and errors of omission, in particular, are a problem.
Automatic or semi-automatic extraction of information from corpora is
very promising, but a vast amount of data is needed for reliable
entries to be constructed on any but the most common words, and this is
currently not easy to obtain for most languages.  
Unless the corpus closely matches the intended text type for the NLP
system, relevant senses of words are likely to be missing.
Furthermore, extraction of lexical semantic information from
corpora has not yet been accomplished on a large scale.
The techniques we describe here rely on the use of MRDs.  These can be
time-consuming to process initially, may contain errors and
inconsistencies, and are of highly variable quality with respect to
their utilisation for NLP.  But they do offer broad coverage and it is
possible to extract quite extensive lexical semantic information. 
MRDs are particularly suitable for providing a labour-saving resource to
augment a manually constructed core lexicon
(for further discussion see \cite{bog+brisc} and \cite{brisc91}).


Earlier work within the ACQUILEX project concentrated on building
monolingual lexical fragments, both to investigate their potential utility 
for tasks
that do not involve translation, 
and as a basis for constructing the multilingual
lexical knowledge base (LKB).  Large scale monolingual
lexicon fragments have been constructed semi-automatically for four languages
(English, Spanish, Dutch and Italian) from MRDs; 
see, for example, \cite{cope92}, \cite{san+poz}, \cite{vossen92} and 
\cite{ageno92}.
Here we are concerned with the use of the LKB to
represent multilingual information in the form of links between monolingual
lexical entries, which we refer to as {\it tlinks} (translation links),
and with the methodology for constructing tlinks from the
available MRD resources.\footnote{Further details 
of the work reported in this paper can be found in \cite{ageno94}
and in project working papers \cite{vossen93}, \cite{ageno93},
\cite{sam94}, \cite{cope+al92}.}

Although there is a considerable amount of published work on the use of
MRDs to 
extract monolingual lexical entries, far less attention has been paid to 
MT lexicons.
Helmreich \etal \cite{helm}
report on the use of MRDs in the Pangloss project, but
this essentially involved manual construction of lexical entries, aided
by a sophisticated user-interface to the MRDs,
although procedures to automate acquisition were being developed.
Neff and McCord \cite{neff}
describe the direct and indirect exploitation of
multiple MRDs to support the English-to-German version of the
Slot Grammar based machine translation
system, LMT.  This builds on the extensive work carried out to
construct a monolingual English lexicon (see \cite{klavans}),  using
information extracted from the bilingual Collins English-German
dictionary to construct transfer relations.
However, Neff \etal \cite{neffetal} report that the 
automatically generated entries often have to be revised by hand.
It seems to be
generally agreed that bilingual MRDs alone are insufficient for constructing
a lexicon for MT and so in the work reported here we have 
followed Neff \etal\ in combining the use of monolingual and bilingual MRDs.
Our aims are somewhat different from theirs, however, 
because we are attempting to
construct a lexical knowledge base that will support bidirectional
translation between three languages in a range of formalisms, rather
than a unidirectional pairing for a single system.  
We also place more emphasis on
detailed lexical semantic information.  

The general strategy we have adopted is to relate lexical entries 
corresponding 
to word senses in Spanish and Dutch
to the English entries, using an approach which 
combines the use of bilingual MRDs with
comparison of syntactic and semantic aspects of the lexical entries 
constructed using the
monolingual dictionaries.
English was chosen as the central language because of the relative richness
of the monolingual lexical entries, which was largely due to the use of
the Longman Dictionary of Contemporary English (LDOCE \cite{ldoce}) and the
extensive work on analysis of LDOCE that has been carried out: see, 
in particular, \cite{wilks}, 
\cite{jac+grover}, \cite{vossenetal89}, \cite{vossen92}.  
Since it is more difficult to extract information
from the available MRDs for other languages, one of the aims of constructing 
a multilingual LKB is to augment the other lexicons by transfer of
English information.

In general, our use of MRDs is aimed at extraction of sufficient information
in order to categorise lexical entries according to predefined linguistically
motivated classes.  Thus, for example, verbs can be distinguished not only
syntactically as intransitive, transitive and so on, but also into semantic
classes such as psychological 
verbs, verbs of perception and so on (see, for example, \cite{levin}).  
Sanfilippo and Poznanski \cite{san+poz} demonstrated how MRDs could be
used to semi-automatically classify verbs into such classes, which
correspond to types in the typed feature structure based 
lexical representation language (LRL) used in the LKB system.
Our methodology for automatically
classifying nouns semantically is based on the extraction of taxonomies from 
MRDs.
For example, the definition of {\it Sauternes} in LDOCE is:
\begin{description}
\item[Sauternes] a type of sweet gold-coloured French wine
\end{description}
It is possible to parse this definition to extract the {\it genus term}, in this 
case {\it wine}, and then to disambiguate this with respect to the senses 
given for {\it wine} in LDOCE, giving {\it wine}\/$^{1}$ 1 (the 
superscript refers to the homonym number and the following numeral to the
sense within the entry).
This sense of wine has the disambiguated genus term {\it drink}\/$^{2}$ 1.
For details of extraction and disambiguation of genus terms, see 
\cite{klavans90},
\cite{vossen90}, \cite{cope90}, \cite{ageno92}.

If we extract genus terms in this way we can derive taxonomies, which are
(basically tree-structured) hierarchies of word senses.  We can use these 
to classify nouns with respect to semantic classes such as 
{\it comestible substance}:
all senses under {\it drink}\/$^{2}$ 1 in the taxonomy will belong to this class,
for example.
As with verbs, such classes can be motivated by considering 
derivational morphology and systematic polysemies
or sense extensions.
For example, most mass terms denoting types of drink can be {\it portioned}
into count nouns denoting a (conventional) portion of that drink:
{\it beer/a beer} etc.  

As discussed in detail by Vossen and Copestake 
\cite{vossen+cope}, there are difficulties
in constructing and interpreting dictionary taxonomies, and the classification
cannot be carried out totally automatically.  It is also comparatively
more difficult to extract taxonomies from dictionaries which do not have 
the controlled vocabulary and semantic coding found in LDOCE.
However, most concrete nouns can be straightforwardly 
located in taxonomies.
Taxonomies are also used to establish inheritance relationships between lexical 
entries represented within the LKB.   Although extraction of the genus term
is the most important step in classifying a sense, and thus establishing its
semantic type, the other parts of the definition, the {\it differentia}, can
also be parsed and provide information which refines the classification.

The representation language used in the LKB,
the LRL, is designed to be quite general, capable
of encoding a variety of linguistic approaches,
following the same sort of philosophy as PATR-II \cite{shieber}
and, like it, based on the use of feature structures (FS) and unification.
The LRL has a relatively rich description language, 
which is geared towards lexical representation since it allows for inheritance
and default inheritance between lexical entries.
The underlying language utilises typed feature structures 
\cite{carp}, and can be used for either rule-based or
constraint-based approaches to parsing and generation.
The extra expressiveness that types provide compared to untyped feature structures is an important component of
our approach to lexical representation.
For ACQUILEX we adopted a common type system to encode 
properties of lexical items in the four languages investigated within
the project.
This ensures compatibility of representation without ruling out language
specific parameterisation where necessary.
A lexical entry for {\it Sauternes}, extracted from LDOCE, is partially shown in 
Figure~\ref{sauternes}.  The types (shown in bold font) are mostly intended to
be language independent with the exception of strings (e.g. the value of ORTH)
and predicates with appended sense identifiers (e.g. 
{\bf sauternes\_L\_0\_0} ---
the `L' identifies the sense as coming from LDOCE; in general, the first integer
stands for the homonym number and the second for the sense number, but here
the 0\_0 indicates that
no sense distinctions were made for this entry).
\begin{figure}
\centering
$\avmplus{\att{lex-noun-sign}\\
         \attvaltyp{ORTH}{{\small\tt "}Sauternes{\small\tt "}}\\
         \attvaltyp{CAT}{noun-cat}\\
         \attval{SEM}{\avmplus{\att{unary-formula-entity-arg1}\\
                  \attvaltyp{PRED}{sauternes\_L\_0\_0}\\
                  \attvaltyp{ARG1}{entity}}}\\
         \attval{RQS}{\avmplus{\att{c\_art\_subst}\\
                  \attvaltyp{ORIGIN-AREA}{french}\\
                  \attval{QUAL}{\avmplus{
                                  \attval{COLOUR\_SPEC}{\avmplus{
                                                   \attvaltyp{COLOUR}{gold}}}\\
                                         \attvaltyp{TASTE}{sweet}}}\\
                  \attvaltyp{PHYSICAL\_STATE}{liquid\_a}\\
                  \attval{FORM}{\avmplus{\attvaltyp{SHAPE}{non-individuated}}}}}}$
\caption{Simplified lexical entry for {\it Sauternes}}
\label{sauternes}
\end{figure} 

One important feature of our approach
is our emphasis on representing detailed lexical semantic information.
We refer to this as {\it relativised qualia 
structure} or RQS \cite{calz}.
The features found in an RQS vary according to the type of the
concept being represented; thus {\sc colour} is an appropriate feature for
physical objects and substances (including liquids like {\it Sauternes}),
while {\sc taste} is only relevant for commonly ingested things.
Some features in the RQS are instantiated by concepts which are
intended to be language independent (e.g. {\bf sweet}) 
while others are language dependent.\footnote{We have, on the
whole, perpetuated the tradition of using English words to
name language independent concepts, partly because of the central status
of the English monolingual lexicon in the project and partly because
it is the language most project members have in common.}
 
Although the RQS framework was originally described \cite{calz}
as being related
to the concept of qualia structure due to Pustejovsky 
(e.g.~\cite{pust91}), it is 
important not to conflate the two: the RQS type system 
has been developed to a large extent
on the basis of analysis of the definitions in dictionaries
and is motivated more by empirical
data than theoretical considerations, while Pustejovsky's
qualia structure is part of his theory of the Generative Lexicon.
There is, of course, considerable debate about whether the sort of information 
we represent in the RQS can properly
be regarded as lexical or whether it is part of real world knowledge and
indeed, whether any distinction can be drawn between the two.
However, it is not clear that this distinction
is of great practical 
significance in our case: we include the RQS information in what we refer to as a 
lexical KB, but maintain a clear separation between it and the 
more conventional lexical information.  
Furthermore, although in a full
MT system it is clear that logical inference mechanisms 
that are not available in the LKB would be
required to act on the conceptual information, this is not a requirement 
for our current work.  
In fact, MRDs can only supply a rather limited amount of
real world knowledge of the sort required to make complex inferences. 
But they do allow word senses (or concepts) to be hierarchically
related to each other and also to be classified with respect to a fixed set of
semantic properties, and the type based formalism of the LKB supports this.
The LRL is actually somewhat less complex than many variants of the
typed feature structure approach advocated for purely linguistic representation
purposes, and the underlying
language has not been extended for the representation of the RQS.


We can regard the RQS as locating a concept in a 
multi-dimensional semantic space, according to the set of features appropriate for 
its type.  
We cannot hope to have a complete account of meaning of a concept
in this manner, but what is required here is categorisation, not complete
decomposition: to the extent that we make use of a language independent set of
types and features, the RQS thus provides a partial interlingua.
The problem of constructing a multilingual lexicon can be seen as relating
concepts which can be expressed lexically in either
the source or target languages or both.
In the simplest cases, translation equivalence
can be represented as a one-to-one mapping between
senses in the monolingual lexicons.  
We will assume for the moment that strictly
translation equivalent senses should
have compatible RQS, though as we will see later this is not always achieved
in practice.  
In any case we do not think of translation equivalence
as identity of concepts: there will inevitably be some
difference in denotation, 
except in the rare cases of concepts with an accepted
expert definition, such as chemical elements. However speakers of the same language
are unlikely to agree precisely on the meaning of a term.  So, 
accepting this indeterminacy, we can take strict translation equivalence 
as implying that the difference in meaning is not significantly greater than 
the monolingual spread.  For a complete
lexicon it would also be necessary to take into account
discourse function, conventional implicature and so on, but we will ignore this
here.


In theory, it might be possible to simply link lexical entries by
comparing their RQSs without the aid of a 
bilingual dictionary.\footnote{Sutcliffe \cite{sutcliffe}
describes the use of weighted features
bundles to represent information extracted from MRDs 
and suggests that comparison of these alone can be used to construct 
a bilingual lexicon, by linking entries with the most similar set of features.}
Unfortunately we have found that
it is not possible to accurately construct translation
equivalence pairings simply by comparing the RQSs of structures in the monolingual 
lexicons, because the information we can extract from the MRDs does not
give a fine enough grain of discrimination and, in many cases, there
is no straightforward one-to-one mapping between lexical entries.
We therefore have to make use of bilingual dictionaries in constructing mappings.
This is discussed in detail in \S\ref{exp}, but first we describe the 
translation link formalism. 

\section{Translation links}
\label{tlink}

Several unification
based formalisms have been proposed 
to model transfer approaches to MT, aiming for
declarativeness and bidirectionality, but allowing sufficient
expressiveness to deal with complex classes of translation equivalence
(e.g. \cite{kaplanetal}, \cite{zajac}, \cite{estivaletal}, \cite{alshawietal}).
In designing the translation link 
formalism we have attempted to maintain these advantages,
but to abstract away from the aspects of these systems which are 
specific to particular MT techniques or details of the grammar formalism adopted.
We have attempted to maximize the functionality of the system with
respect to the expression of linguistic and lexicographic
generalisations in order to produce an LKB
which would support a variety of approaches to MT, although
it is naturally most appropriate to the more lexicalist frameworks.

We define lexical translation equivalence in terms of cross-linguistic
links, {\em tlinks}, stated in terms of lexical entries in the monolingual
lexicons and of classes of lexical entries.
Because tlinks are defined
in terms of inheritance from lexical entries and rules,
translation equivalence can be stated through direct reference to
properties of word syntax and semantics as they appear in the
monolingual lexical descriptions. The sharing of information
structures between tlinks and the lexicons means 
that
the description of translation equivalence is as compact as
possible and ensures that the
multilingual and monolingual components are compatible. 
The use of the typed feature structure formalism makes it easy to define
classes of translation equivalence appropriate for particular classes of
mismatch.

The full translation link formalism is described 
in \cite{cope93}, which also
discusses how translation links can be interpreted as constraints which must hold
between source and target language structures in order for them to be translation
equivalents.  
The formalism can be used as the basis for a constraint
based transfer system, although for practical use it would be necessary to pay
much more attention to control aspects.
Some complex translation mismatch cases are described in \cite{sanetal}
and \cite{cope+san}.
Here we will concentrate on informally describing the simpler classes
of tlinks, since these make up the vast majority of cases, including all those
we can currently extract semi-automatically from MRDs.
However at the
end of the section we give a sketch of a more complex head-switching example.
We begin with an example of a tlink for 
English {\it wine} $\approx$ Spanish {\it vino}, which is written as 
follows in the description language:
\begin{verbatim}
wine_1 / vino_1 :
simple-tlink.
\end{verbatim}

This tlink directly relates monolingual lexical entries
and expands into a typed FS which includes both lexical signs.
If we assume for the moment 
that this is the only English-Spanish tlink for {\it wine}, it
describes a constraint which states that any English language 
parsed structure
which contains the lexical sign for this sense of {\it wine}
has to be paired with a Spanish structure containing the sign for
{\it vino}, with the same variable being used in the semantics of both signs.  
This equivalence of variables can be used to
drive translation, as in 
Shake'n Bake transfer \cite{whitelock}, although the 
implementation used in the LKB is somewhat different. 
The form that the tlink takes in terms of typed feature structures is 
determined by its type, {\bf simple-tlink}, which is a subtype of {\bf tlink}.

Although this tlink relates lexical entries directly, 
the formalism has to allow for greater flexibility.
All tlinks are defined as FSs of type {\bf tlink}, which contains the source
FS (path {\tt <} {\sc sfs} {\tt >}) and the target 
FS ({\tt <} {\sc tfs} {\tt >}) which are both of type {\bf rule}.
This is the type which is used to encode monolingual lexical rules and 
grammar rules: 
minimally, a rule establishes a correspondence between an input sign
(1) and an output sign (0).
The rule-inputs of tlinks ({\tt <} {\sc sfs} 1 {\tt >} and
{\tt <} {\sc tfs} 1 {\tt >})
are meant to be instantiated by unification with lexical entries for
word senses in the source and target languages; the rule-outputs 
({\tt <} {\sc sfs} 0 {\tt >} and
{\tt <} {\sc tfs} 0 {\tt >}) provide the
translation equivalent structures when this is done.\footnote{Tlinks are both
symmetrical and reversible; we use the terminology source,
target, input and output solely for ease of exposition.}
It is the pairing between the rule outputs which is used during
translation itself: tlinks are applied by 
unification of their source language output structure 
with an instantiated sign in
the FS representing the parse of the source sentence,
giving a target language output sign which must
correspond to part of the target language structure.
Thus tlink application
creates a set of constraints which must be
satisfied by the target language structure and which can be
resolved to generate the target language sentence.  However, the
explicit connection to the monolingual lexical entries allows the
description of tlinks to be compact and consistent with the monolingual
lexicon, while being sufficiently expressive to allow for translation
mismatches. 

The concept of translation equivalence is constrained by defining an
inheritance network of tlink types encoding generalisations relative
to classes of crosslinguistic equivalences. The commonest and simplest
cases of translation equivalence, such as the example given above,
can be represented as {\bf simple-tlink}s.
A {\bf simple-tlink} is applicable in the case where two lexical
entries which denote one-place predicates (nouns, etc.) are
straightforwardly translation equivalent, without any transformation
being necessary. 
The semantic variables
relative to the semantic entity described by two output structures are
specified to be identical.
The particular paths equated will depend on the way that the
type system is used to encode semantic structure.
Rather than showing the FS expansions of tlinks we will use a diagrammatic
representation as in Figure~\ref{vino} which shows the {\bf
simple-tlink} for English:{\it wine} $\approx$ Spanish:{\it vino}. 
The formal semantic structure corresponding to each sign is shown in the
figure using a linearised form of the representation
(the semantic theory used is Indexed Language, InL,
\cite{inl} --- the actual encoding
uses FSs but this notation is considerably easier to read).
The coindexation between the arguments of the
predicates of the source and target language 
is indicated by the use of the same variables: in this case, no other 
information is shared.
\begin{figure*}
\setlength{\unitlength}{0.9mm}
\begin{picture}(125,25)(-5, 10)
\thinlines    \put(5,25){\makebox(0,0){SFS1}}
              \put(5,18){\makebox(0,0){wine}}
              \put(22,30){\makebox(0,0){identity}}
              \put(10,25){\vector(1,0){25}}
              \put(40,25){\makebox(0,0){SFS0}}
              \put(40,18){\makebox(0,0){wine}}
              \put(40,10){\makebox(0,0){$[ x ][ \mbox{wine\_1}(x) ]$}}
              \put(50,35){\makebox(0,0){\bf tlink}}
\thicklines
              \put(75,25){\vector(-1,0){30}}
              \put(45,25){\vector(1,0){30}}
\thinlines
              \put(80,25){\makebox(0,0){TFS0}}
              \put(80,18){\makebox(0,0){vino}}
              \put(80,10){\makebox(0,0){$[ x ][ \mbox{vino\_1}(x) ]$}}
              \put(97,30){\makebox(0,0){identity}}
              \put(110,25){\vector(-1,0){25}}
              \put(115,25){\makebox(0,0){TFS1}}
              \put(115,18){\makebox(0,0){vino}}
\end{picture}
\caption{Figurative representation of translation link relating {\it
wine} and {\it vino}}
\label{vino}
\end{figure*}

Some restrictions on translation can be expressed by making the target
or source FSs of tlinks more specific. For example, we can define a
type {\bf human-tlink} and state as a constraint that the values for
the {\sc sex} feature must be the same in the translation equivalent
feature structures:
\begin{verbatim}
human-tlink (simple-tlink)
< SFS : 1 > = human
< TFS : 1 > = human
< SFS : 0 : RQS : SEX > = < TFS : 0 : RQS : SEX >.
\end{verbatim}
A tlink of this type would be suitable for establishing equivalences
such as that between the word {\it teacher} in English and its
translation in Spanish as either {\it maestro} or {\it maestra}. The
restriction that {\it maestro} denotes a male teacher and {\it
maestra} a female one --- i.e. the values for \verb+< RQS : SEX >+ in
the FS descriptions for the two words are {\bf male} and {\bf female}
respectively --- follows from the use of tlinks of type {\bf
human-tlink} to relate {\it teacher} with {\it maestro} and {\it maestra}:
\begin{verbatim}
teacher / maestro :
human-tlink.

teacher / maestra :
human-tlink.
\end{verbatim}
The path equation induced by the {\bf human-tlink} will ensure that
translation equivalent words denote individuals of the same natural
gender.\footnote{The morphological generalisation involved in
this example can
be captured, making it unnecessary to manually specify both tlinks.}

Somewhat rarer and more complex cases of linking arise when a
translation mismatch involves operations on FSs.  For
example, the equivalence class resulting from translation pairs such
as `English:{\it furniture} $\approx$ Spanish:{\it muebles}' can be
represented by establishing a link between a word and its translation
in the plural form. In this case the equivalence holds between a basic
lexical entry and a lexical entry after rule application. In our
approach, this behaviour can be modelled straightforwardly, by
instantiating one half of the tlink with the appropriate lexical rule.
This is shown in the tlink below where the
target-language side (\verb+< TFS >+) inherits from the FS
representation for the plural rule whose input is the tlink's
target input ({\tt mueble}) and output is the tlink's
target output.
\begin{verbatim}
furniture / mueble :
tlink
< SFS : 0 > = < SFS : 1 >
< TFS > <= plural <>.
\end{verbatim}
Note that the lexical/morphological rule for plural
formation used in the tlink is needed anyway for use in
parsing/generation, so that its use in tlinks does not involve
introduction of elements other than those needed in the monolingual
grammars. The tlink can be represented figuratively as shown in
Figure~\ref{furniture} 
where unlabelled arrows indicate token identity between FSs.

\begin{figure*}
\setlength{\unitlength}{0.9mm}
\begin{picture}(125,25)(-5, 10)
\thinlines    \put(5,25){\makebox(0,0){SFS1}}
              \put(5,18){\makebox(0,0){furniture}}
%             \put(22,30){\makebox(0,0){identity}}
              \put(10,25){\vector(1,0){25}}
              \put(40,25){\makebox(0,0){SFS0}}
              \put(40,18){\makebox(0,0){furniture}}
              \put(40,10){\makebox(0,0){$[ x ] [\mbox{furniture}(x) ]$}}
              \put(50,35){\makebox(0,0){\bf tlink}}
\thicklines
              \put(75,25){\vector(-1,0){30}}
              \put(45,25){\vector(1,0){30}}
\thinlines
              \put(80,25){\makebox(0,0){TFS0}}
              \put(80,18){\makebox(0,0){muebles}}
              \put(80,10){\makebox(0,0){$[ x ] [\mbox{}^\star\mbox{mueble}(x) ]$}}
              \put(97,30){\makebox(0,0){plural}}
              \put(110,25){\vector(-1,0){25}}
              \put(115,25){\makebox(0,0){TFS1}}
              \put(115,18){\makebox(0,0){mueble}}
\end{picture}
\caption{Figurative representation of translation link relating {\it
furniture} and {\it muebles}}
\label{furniture}
\end{figure*}

Since the singular form {\it mueble} would not unify with the
feature structure at the end of the output path \verb+< TFS : 0 >+, a
translation of {\it mueble} as {\it furniture} would not be generated
and another tlink would be required to relate the two words when translating
a phrase such as {\it a piece of furniture}. 
The tlink mechanism allows this to be done in a variety of ways.  The
exact phrase could be encoded:
\begin{verbatim}
piece_1 of_1 furniture_1 / mueble_1 :
phrasal-source-tlink .
\end{verbatim}
where the type {\bf phrasal-source-tlink} specifies that the English words are
combined by ordinary grammar rules to produce that exact phrase.
In general, we want to weaken this
to allow for the possibility of material being interpolated
(e.g. {\it piece of English furniture}): we do this essentially by establishing
a semantic rather than a syntactic connection between the lexical signs 
(see \cite{cope93} for details).
Furthermore we would like to allow for a range
of individuating words: {\it item}, {\it article} and even 
{\it stick} are possible instead of {\it piece}.
Such a tlink would relate a lexical item to a feature structure representing a
partially specified phrase whose full instantiation is established contextually, 
since the determination of the particular individuating word is properly part
of the monolingual rather than the bilingual lexicon.  The formalism allows
for underspecified placeholders for lexical items: for example
\begin{verbatim}
individuator of_1 furniture_1 / mueble_1 :
phrasal-source-tlink .
\end{verbatim}
where {\tt individuator} 
is a FS which subsumes the FSs for {\it piece}, {\it item}, etc.
Of course, we could generalise over cases of this sort and
define a tlink type which incorporates the lexical sign for {\it of} 
rather than
mentioning it explicitly each time.

We also make use of {\it tlink-rules}, which are
FSs which can be regarded either
as relating tlinks, or as relating monolingual 
lexical or morphological rules.  For example, the portioning
sense extension mentioned in the introduction is found both in English and in 
Dutch, so that if we have a tlink relating the mass sense of {\it gin} to
{\it jenever} we can automatically generate the relationship between the count 
senses of both terms.\footnote{In some cases diminutive formation in
Dutch appears to have the effect of portioning, for example, {\it biertje}
can be translated as {\it glass of beer} \cite{vossen91}.}
More interesting cases involve some mismatch between 
languages, so for example, 
while in English nouns denoting fruits can also
normally be applied to the tree or plant which produces them ({\it apple},
{\it apricot}, etc.), in Spanish the same polysemy normally involves a change in 
gender or suffixation 
({\it manzana}, {\it manzano}; {\it albaricoque}, {\it albaricoquero}).
A tlink-rule expressing this relationship is shown diagrammatically in
Figure~\ref{apple}.
Sometimes affixation in Spanish seems to
correspond to compounding in English:
for example, the suffix {\it -al} applies to plants
to give a sense denoting a group of such plants, that is a field
or plantation, etc.  For example, {\it aguacate} ({\it avocado}) takes the
suffix to form {\it aguacatal} ({\it avocado plantation}).
Marti and Soler 
\cite{mart+sol} discuss these examples and a number of others which 
have been found by analysis of the bilingual
dictionary Vox-Harrap's Esencial \cite{voxharraps}.
This is an important issue for automatic acquisition of tlinks, because
it indicates that
some translation equivalences can be systematically
derived from others.  
\begin{figure*}
\setlength{\unitlength}{0.9mm}
\begin{picture}(140,73)(-20,-35)
\thinlines    \put(5,25){\makebox(0,0){SFS1}}
              \put(5,18){\makebox(0,0){apple1}}
              \put(10,25){\vector(1,0){25}}
              \put(40,25){\makebox(0,0){SFS0}}
              \put(40,18){\makebox(0,0){apple1}}
              \put(50,35){\makebox(0,0){\bf simple-tlink}}
\thicklines
              \put(55,25){\vector(-1,0){10}}
              \put(45,25){\vector(1,0){10}}
\thinlines
              \put(60,25){\makebox(0,0){TFS0}}
              \put(60,18){\makebox(0,0){manzana}}
              \put(90,25){\vector(-1,0){25}}
              \put(95,25){\makebox(0,0){TFS1}}
              \put(95,18){\makebox(0,0){manzana}}

              \put(5,15){\vector(0,-1){35}}
              \put(-10,0){\makebox(0,0){\shortstack{English\\ fruit-\\ tree\\ alternation}}}
              \put(95,15){\vector(0,-1){35}}
              \put(110,0){\makebox(0,0){\shortstack{Spanish\\ fruit-\\ tree\\ suffixation}}}

              \put(5,-25){\makebox(0,0){SFS1}}
              \put(5,-32){\makebox(0,0){apple2}}
              \put(10,-25){\vector(1,0){25}}
              \put(40,-25){\makebox(0,0){SFS0}}
              \put(40,-32){\makebox(0,0){apple2}}
              \put(50,-15){\makebox(0,0){\bf simple-tlink}}
\thicklines
              \put(55,-25){\vector(-1,0){10}}
              \put(45,-25){\vector(1,0){10}}
\thinlines
              \put(60,-25){\makebox(0,0){TFS0}}
              \put(60,-32){\makebox(0,0){manzano}}
              \put(90,-25){\vector(-1,0){25}}
              \put(95,-25){\makebox(0,0){TFS1}}
              \put(95,-32){\makebox(0,0){manzano}}
\end{picture}
\caption{Tlink rule relating fruit and tree senses of {\it apple} to {\it manzana/o}}
\label{apple}
\end{figure*}

Finally, we briefly consider a head switching example, which further
demonstrates the possibility of automatically deriving 
complex relationships from simple ones
and also illustrates that the tlink mechanism is adequate to deal with
one of the standard test cases for MT formalisms.
English verbs of manner of motion (e.g. {\it swim}, {\it stagger})
can occur with a locative expression
describing a completed path.
In many other languages, including the Romance languages and Japanese,
this pattern is not possible \cite{talmy85}.  Here we will concentrate on
Spanish, 
where the manner of motion is expressed with an adverbial, while the
main verb conveys the path.  For example,
\exnum{+1}{a} is usually translated as
\exnum{+1}{b} (which is literally translated as \exnum{+1}{c}). 
\begin{ex}
\begin{subex}
Kim swam across the river.
\end{subex}
\begin{subex}
Kim cruz\'{o} el r\'{\i}o nadando.
\end{subex}
\begin{subex}
Kim crossed the river swimming.
\end{subex}
\end{ex}
The use of tlinks to treat this example was discussed in detail in 
\cite{sanetal}.  Some details of the tlink formalism have
changed, but we will follow the same semantic analysis here.
The verb semantic representation uses a neo-Davidsonian approach 
with proto-roles \cite{dowty} being used to express thematic relations.
The semantics is described in detail in \cite{sanvsem}.
We assume that strict intransitive 
motion verbs such as {\it swim} are related by lexical rule
to entries
subcategorized for a PP expressing a path.  The details
of this are unimportant here, but we assume that in Spanish this rule
cannot apply to manner of motion verbs when the path is bounded.
The logical form for \exnum{0}{a}, expressed in a linearised notation,
is:
\begin{wideequation}
\begin{equation}
\exists e,y [\mbox{swim}(e) \wedge
\mbox{p-agt-cause-move-manner-path}(e,\mbox{kim})
\wedge \mbox{across}(e,y) \wedge \mbox{river}(y)] 
\end{equation}
\end{wideequation}
We assume that the gerundive in the Spanish sentence
expresses a distinct
event, which is related to the main verb event by a predicate {\bf
while} indicating that the first event is temporally contained within the  
second. 
Thus \exnum{0}{b} has the following semantics:
\begin{splitmath}
\exists e,e',&y [\mbox{cruzar}(e) \wedge
\mbox{p-agt-cause-move-path}(e,\mbox{kim})
\wedge \mbox{p-pat-path}(e,y) \\ \wedge\ 
\mbox{while}(e', e) \wedge \mbox{nadar}(e') \wedge
\mbox{p-agt-cause-move-manner}(e',\mbox{kim}) \wedge \mbox{r\'{\i}o}(y)]
\end{splitmath}

The translation link which expresses the equivalence has to relate the
pair of lexical entries for {\it swim} (with completed path) and {\it
across} to the pair for {\it cruzar} and {\it nadando}.
As above, we achieve this by relating phrases which are
constrained to include FSs corresponding to 
those lexical items in the relevant semantic
relationship, without specifying the syntactic structure directly.
The tlink is shown diagramatically in Figure~\ref{swacfig1}
(`incl' indicates the inclusion relationship).
The translation of {\it the river} as {\it el r\'{\i}o} occurs as
usual, and the coindexation on the variables ensures that it is
expressed as an argument to {\it across} in the English sentence and to
{\it cruzar} in Spanish.
\begin{figure}
\setlength{\unitlength}{0.95mm}
\begin{picture}(120,64)(-10,-27)
\thinlines 
              \put(5,25){\makebox(0,0){SFS1/INC1}}
              \put(5,18){\makebox(0,0){swim (+path)}}
              \put(-5,12){\makebox(0,0)[l]{\small $[e] [\mbox{swim}(e)\,\wedge$}}
              \put(-5,8){\makebox(0,0)[l]{\small $\mbox{p-agt-cmmp}(e,x)\,\wedge$}}
              \put(-5,4){\makebox(0,0)[l]{\small $\mbox{p-pat-p}(e,y)]$}}
              \put(22,30){\makebox(0,0){incl}}
              \put(15,25){\vector(1,0){20}}
              \put(40,25){\makebox(0,0){SFS0}}
              \put(40,18){\makebox(0,0){vpsign}}
              \put(55,35){\makebox(0,0){\bf tlink}}

              \put(22,0){\makebox(0,0){incl}}
              \put(15,-5){\vector(1,1){20}}
              \put(5,-5){\makebox(0,0){SFS2/INC2}}
              \put(5,-12){\makebox(0,0){across}}
              \put(5,-18){\makebox(0,0){\small $[e] [\mbox{across}(e,y)]$}}
\thicklines
              \put(65,25){\vector(-1,0){20}}
              \put(45,25){\vector(1,0){20}}
\thinlines
              \put(70,25){\makebox(0,0){TFS0}}
              \put(70,18){\makebox(0,0){vpsign}}
              \put(87,30){\makebox(0,0){incl}}
              \put(95,25){\vector(-1,0){20}}
              \put(105,25){\makebox(0,0){TFS1/INC1}}
              \put(105,18){\makebox(0,0){nadando}}
              \put(95,12){\makebox(0,0)[l]{\small $[e'] [\mbox{while}(e,e')\,\wedge$}} 
              \put(95,8){\makebox(0,0)[l]{\small $\mbox{nadar}(e')\,\wedge$}}
              \put(95,4){\makebox(0,0)[l]{\small $\mbox{p-agt-cmm}(e',x)]$}}
              \put(87,0){\makebox(0,0){incl}}
              \put(95,-5){\vector(-1,1){20}}
              \put(105,-5){\makebox(0,0){TFS2/INC2}}
              \put(105,-12){\makebox(0,0){cruzar}}
              \put(95,-18){\makebox(0,0)[l]{\small $[e] [\mbox{cruzar}(e)\,\wedge$}}
              \put(95,-22){\makebox(0,0)[l]{\small $\mbox{p-agt-cmp}(e,x)\,\wedge$}}
              \put(95,-26){\makebox(0,0)[l]{\small $\mbox{p-pat-p}(e,y)]$}}
\end{picture}
\caption{Manner of movement with completed path tlink.  The following 
abbreviations are used in the semantic representation: 
p-agt-cmmp for p-agt-cause-move-manner-path,
p-agt-cmm for p-agt-cause-move-manner,
p-agt-cmp for p-agt-cause-move-path, and
p-pat-p for p-pat-path.}
\label{swacfig1}
\end{figure}


Clearly it would be very time-consuming to have to construct such
relationships manually for every possible
pairing of a manner of a movement verb with a  
preposition expressing a path.  
Instead, we generate this equivalence automatically,
from the straightforward relationship between strict intransitive {\it
swim} and {\it nadar} plus a relationship between {\it cruzar} and 
an underspecified English movement verb taking an {\it across} PP as a
path argument.
This is done by a tlink rule which relates the English lexical rule
which creates the path-taking manner-of-movement
verb sense 
to the Spanish suffixation with {\it +ndo} (see \cite{cope+san},
\cite{sanetal}).\footnote{However,
because it appears that there are a limited
number of possible paths that can be expressed in natural language, it
would perhaps be more elegant to make use of a partial interlingua
here, and relate both {\it across} and {\it cruzar} to a single
underlying primitive expressing the path in the monolingual lexicon.
This would enable us to avoid having to explicitly specify the 
{\it across}/{\it cruzar} link.} 


\section{Constructing tlinks}
\label{exp}

Since the monolingual ACQUILEX LKB on the whole uses dictionary sense
distinctions, the linking between monolingual entries has to make
reference to these.  The following example illustrates the sort of
tlink we aim to derive:
\begin{verbatim}
wine_L_1_1 / vino_X_1_1 :
simple-tlink.
\end{verbatim}
This tlink directly relates lexical entries corresponding to
dictionary senses: \verb+vino_X_1_1+ stands for the VOX 
monolingual Spanish dictionary \cite{vox} sense
{\it vino}\/$^1$ 1, L stands for LDOCE, as before.
Since we were only dealing with nouns denoting physical objects, there
was no need to attempt to determine complex variable equivalences ---
thus all the tlinks derived could be assumed to have the same sort of
equivalence as a {\tt simple-tlink}.  Various other subclasses of
tlinks are discussed below: these were defined either to record
information about how the tlink was derived or to indicate its
partiality --- they do not affect the internal structure.

Two sets of experiments were carried out on semi-automatically
deriving translation links: 
one linking Dutch and English lexicons, the other Spanish and English.
In both cases, a similar basic approach was taken, which we describe initially,
followed by details
of the Dutch-English and Spanish-English experiments.
The methodology involves looking at each source sense in turn and attempting
to find corresponding senses (possibly modified or 
combined into phrases) in the target language.
As we mentioned
in the introduction, although in principle it might be possible to relate
monolingual lexical entries purely by comparison of their RQSs, in practice 
the definitions are too coarse grained for this to be possible, especially
since lexicographers
may choose to highlight different features in their definitions, and thus 
we have to check for compatibility of structures, rather than an exact match.
Furthermore, since many lexical entries only have phrasal equivalents, we
have to use bilingual dictionaries to constrain the search space.

The use of bilingual dictionaries means we have to consider the following difficulties:
\begin{enumerate}
\item There is no direct correspondence between the senses given in the monolingual
dictionaries, the bilingual dictionaries and the translations. 
\item A source sense may be missing in the bilingual dictionary.
\item A source sense may be 
found in the bilingual dictionary but its translation is not
found in the target monolingual dictionary. 
\item A source sense is found in the bilingual dictionary but it is translated by
a phrase.
\item A translation is restricted by a label (such as {\it med.} for medical).
\end{enumerate}

The sense correspondence problem 
can be appreciated most easily by looking at an example
(taken from \cite{vossen93}):

\begin{center}
{\small
\begin{tabbing}
Entry X\=01$\langle$object$\rangle$XXXX\=Entry X\=01$\langle$object$\rangle$  \=
TranslationsXXXX\= Entry X\=\kill
Van Dale monolingual\>\>VanDale Dutch-English bilingual\>\>\>LDOCE\\
Entry\>Senses\>Entry\>Senses\>Translations\>Entry\>Senses\\
doos\>01$\langle${\it object}$\rangle$\>doos\>01$\langle${\it object}$\rangle$\>box\>box\>
01 {\it container}\\
\>\>\>\>\>\>02 {\it the contents}\\
\>\>\>\>\>\>03 {\it room}\\
\>\>\>\>\>\>04 {\it piece of metal}\\
\>\>\>\>\>\>05 {\it television}\\[0.1in]
\>02$\langle${\it toilet}$\rangle$\>\>02$\langle${\it
toilet}$\rangle$\>loo (BE)\>loo\>01 {\it lavatory}\\
\>(informal)\>\>(informal)\>lav (BE)\>GAP\\
\>\>\>\>John (AE)\>John\>01 {\it man}\\
\>\>\>\>\>\>02 {\it lavatory}\\
\>\>\>\>can (AE)\>\ldots\\[0.1in]
\>GAP\>\>03$\langle${\it jail}$\rangle$\>clink (BE)\>\ldots\\
\>\>\>(informal)\>junk (BE)\>\ldots\\
\>\>\>\>the slammer (AE)\>\ldots\\
\>\>\>\>the can (AE)\>\ldots
\end{tabbing}}
\end{center}


The words in italics are included from the definitions in order to
indicate the senses involved, the words
in angle brackets are English glosses of the Dutch.  
We have not listed all the LDOCE senses
for {\it can}, {\it clink}, {\it junk}, etc. because there are too many
irrelevant ones.
It is clear that we have to isolate the 
appropriate sense of {\it box}, {\it can}, etc., in order to produce the
correct tlinks and on the whole bilingual dictionaries do not provide an
adequate source of information for doing this.  
This example also illustrates the problem of gaps in the dictionaries:
{\it lav} is not given as a headword in LDOCE, for example.
(This could alternatively be regarded as an example of a bilingual
dictionary giving
an apparently inappropriate translation, since {\it lav} is not a
common word in contemporary British English in our experience.)
Finally, it shows examples of phrases used in
the translation field: {\it the slammer} and
{\it the can}. 
However these are not genuine phrasal translations but instead indications
of a restriction on the
use of the terms in the monolingual grammar since {\it the} is obligatory
with these senses:
\begin{ex}
* He was in a can.
\end{ex}


We originally attempted to tackle the
bilingual sense disambiguation problem by developing a utility program, LUCIFER,
for determining the best match among multiple target lexical
entries, by comparing them for
their similarity to the source.
For example, when constructing a tlink for the first sense of {\it doos} 
given in the VanDale monolingual dictionary, its FS would be compared with
the FSs generated from LDOCE for the different senses of {\it box} and the most
similar proposed to the user as the appropriate translation.
LUCIFER was designed to learn which features in the sign were the most reliable 
indicators from user feedback accepting or rejecting the proposed tlinks
(see \cite{cope+al92}).  However 
this technique has not been exploited to the limit because
we adopted the methodology of linking semantically restricted subsets of the
monolingual lexicon.  It was not feasible to link entire dictionaries
given the limited resources of the project
so
semantic selection of subsets was necessary to
ensure that compatible sets of words were used for each language. 
This preselection greatly reduced the ambiguity problem.  
For example, 
English {\it dish} in the food sense can be translated as {\it gerecht} in Dutch
but the container sense is translated as {\it schaal}.  By preselecting the
food subset, we ensure that we consider only the food senses, 
that is {\it dish}\/$^{1}$ 2 and {\it gerecht}, but exclude {\it schaal}.
The semantic classification used in determining the subset was derived
semi-automatically from the MRDs, primarily
from the automatically derived taxonomies mentioned above
\cite{vossen90}, \cite{cope90}, \cite{ageno92}.

The work reported here relates to noun senses denoting foods or drinks.
Relatively 
few words have multiple senses denoting foods or drinks, with the exception
of regular polysemy, in particular the portioning sense extension mentioned
in the introduction.  The type/kind usages are also occasionally given 
distinct senses and sometimes the two are conflated, as in the LDOCE example
below. 
\begin{description}
\item[brandy 1]  a strong alcoholic drink \ldots
\item[brandy 2] a type or single drink of this
\end{description}
Excluding these cases, we were left with very few examples of
ambiguity to resolve with LUCIFER because we had, in effect, preempted  
its functionality.
In fact,
in the work reported in detail below, the Dutch-English experiment relied
entirely on the disambiguating effect of the taxonomies, with manual intervention
to resolve the remaining distinctions.

Working from a subset is effectively equivalent to taking the semantic 
classification as an absolute restriction on classifying lexical entries as
translation equivalent.  For example, food nouns in English
could only be related to food nouns in Spanish.  It is clear that this might be 
overrestrictive, since concepts like {\it soup}, {\it broth} and {\it beef tea}
are somewhat borderline between foods and drinks, and one can imagine
discrepancies in classification between lexicographers.  There will also
be some errors in the automatic assignments of semantic types to
senses. 
This will tend to make the results reported here
worse than would be expected for a more flexible
approach which allowed links to be proposed between senses with different 
semantic types if no other match could be found.  

We now turn to the actual experiments, which although adopting the 
same basic approach differ considerably in detail, partly because of the
nature of the dictionary resources available and the difference between 
Spanish and Dutch with respect to noun-noun compounds. 
These sections are 
summaries of 
\cite{vossen93}, \cite{ageno94}, \cite{sam93} and \cite{sam94}.
Both experiments relied on the same subsets of English noun lexical
entries, extracted from LDOCE.  For this experiment the
subsets were extracted according to taxonomic information alone, for
example the 
drink subset consists of all senses found under the relevant sense of 
{\it drink} in the taxonomy.
The LDOCE drinks subset contained 
192 entries, the food subset 260 entries.

\subsection{Dutch/English}
\label{dutch}

The monolingual Dutch dictionary used to extract the subset was the Van Dale
dictionary of contemporary Dutch \cite{vand},
the bilingual dictionaries used to construct tlinks were the
Van Dale Dutch-English \cite{vde}
and English-Dutch \cite{ved} which we will abbreviate as VDE and VED.  
The bilingual dictionaries are claimed by Van Dale to be largely
compatible with the monolingual dictionary, since they are based on a single 
database of Dutch.
There is a considerable
disparity in size between the monolingual VanDale which contains about
61000 noun entries and LDOCE with 24000.

Vossen \cite{vossen93}
analysed the translations in the VDE for matches to LDOCE and found
that of 51500 simple
translation fields for nouns\footnote{Complex translation fields, where 
information is conflated according to various lexicographic conventions, 
are excluded from this figure but were parsed
for the tlink construction
experiment.}, only 14000 corresponded to LDOCE entries.
Thus only 27\% of VDE translations are found directly in LDOCE and 
only 60\% of LDOCE entries are found as translations in the VDE.  Most of the
mismatches are phrases of some sort, many being adjectivally modified nouns,
or noun-noun compounds of varying degrees of transparency.  For example:
{\it dierennummer}/{\it animal act}, {\it dierenverzorging}/{\it animal care},
{\it dierenkliniek}/{\it animal clinic}, {\it dierentaal}/{\it animal language}.

The VDE and the VED both give two types of translation: main translations, which
are supposed to be applicable in most contexts, and secondary translations, 
which are more specific or restricted.  The latter are always given as 
alternatives to main translations.  For example, {\it baas} (owner of an animal)
is given the main translation {\it owner} and secondary translations {\it master}
and {\it mistress}.  No distinction was made between main translations and
secondary translations for this experiment (but see \S\ref{res} below).
Various labels are also found on translations: for instance,
in the translations of {\it baas}, {\it master}
and {\it mistress} were labelled m. and f. for masculine and feminine.
Vossen \cite{vossen93}
discusses this in some detail, but since a large set of labels were used
and they were applied somewhat unevenly, they were ignored for this experiment.

The size of the Dutch subset reflected the larger coverage of the
VanDale monolingual when compared to LDOCE, since the {\it drank} subset had
214 entries (compared to 192) and the {\it voedsel} subset 476
(compared to 260).
Rather than comparing LKB entries, Vossen constructed tlinks 
from a database of dictionary entries augmented with the derived
taxonomic information.  
The disambiguation depended entirely on the assumption that only one
sense would be found within the target subset.
The tlinks were generated completely automatically.

Vossen distinguished between four classes of tlink for this experiment:
\begin{description}
\item[simple-tlink] This was applicable
if a lexical entry from the source subset could be
linked by the bilingual dictionary to a lexical entry found in the target subset.  
In the majority of cases, lexical entries will
correspond to single orthographic words, although some multiword compounds 
are found, especially in LDOCE.
\item[orth-tlink] This was used for the 
cases where no entries were found in the bilingual dictionaries
but the source and target subsets contained lexical entries with the same, or
very similar orthography.  These were nearly all cases where the entry
corresponded to a loan word in one or both languages: e.g. {\it Chianti}.
These {\bf orth-tlink}s are essentially 
equivalent to {\bf simple-tlinks}, but it was
useful to record that they were derived without a bilingual dictionary.
\item[compound-tlink]
There were many cases where translation fields contained
Dutch compounds 
which were not present in the monolingual dictionary and hence not in the 
subset.  
For example, the English {\it creme de menthe} has the translation {\it
pepermuntlikeur} in the VED, but {\it pepermuntlikeur} is not found as
an entry in the VanDale monolingual.
Dutch compounds can be morphologically complex, sometimes containing
plural forms or a binding morpheme.  These were analysed into parts
which were checked for inclusion in the subset.  Tlinks constructed in this
way were given the type {\bf compound-tlink}.  Some examples are given below: 
note that these are not fully analysed since the part in brackets is
not found in the subset and not disambiguated.  Note also that this procedure 
generates multiple tlinks when more than one of the component parts is 
found in the subset, which may lead to incorrect results.
\begin{verbatim}
creme_de_menthe_L_0_0 / likeur_V_0_1 (pepermunt):
compound-tlink.

applejack_L_0_0 / wijn_V_0_1 (appelbrande):
compound-tlink.

applejack_L_0_0 / brandewijn_V_0_1 (appel):
compound-tlink.
\end{verbatim}
\item[phrasal-tlink]
These were treated similarly to compounds, resulting in tlinks such as 
\begin{verbatim}
sarsaparilla_L_0_0 / frisdrank_V_0_1 (met sarsaparillasmaak):
phrasal-tlink.
\end{verbatim}
\end{description}
Further examples of tlinks are given in the appendix.
Both compound and phrasal tlinks as described here
are essentially partial --- 
their utilisation is discussed in \S\ref{res}, below.
No rules have yet been implemented to deal with plural forms and
derivations
in translation fields.

The results for the four subsets are summarised in Table~\ref{de}.
In many cases multiple tlinks were produced for a sense, because multiple 
translations were given in the bilingual dictionary.
Note that the senses not linked in this experiment could have been linked
via their taxonomic parents, giving another form of partial tlink.  This idea is
described in more detail in the next section since it was extensively used in
the Spanish experiment.
The noticeably poorer linking rate in the food subsets is 
discussed below, in \S\ref{res}. 
\begin{table}
\caption{Dutch/English tlinks} 
\label{de}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lllllll}
\hline
source           & senses & simple & phrasal & compound & orth & \% senses\\ 
subset           &        & tlinks & tlinks  & tlinks  & tlinks &linked \\\hline
Van Dale drank   & 214    & 116    & 104     &  0       &   11 & 63\%\\
Van Dale voedsel & 476    & 170    & 268     &  10      &    4 & 53\%\\
LDOCE drink      & 192    & 134    & 43      &  39      &    3 & 65\%\\
LDOCE food       & 260    & 72     & 124     &  114     &   14 & 55\%\cr
\hline
\end{tabular*}
\end{table}

A small portion of the LKB entries could not be linked because of gaps in the
bilingual dictionaries: going from English to Dutch  55 out of 452 
senses, going from Dutch to English 66 out of 1050 senses.  
Most of these are true gaps in the bilingual
dictionaries, e.g. {\it Bordeaux}, {\it green tea}, {\it Irish stew}, 
{\it moussaka},
{\it schuimbier}, {\it stortebier}, {\it dressing}.  
Some of these gaps are
nevertheless resolved because of the orthographic equivalence heuristic.
Some LKB entries have not been
found because they were in an inflected form, e.g. 
{\it refreshments}, 
others have not been found because their entries are
simple cross references e.g. {\it levensbenodigdheden} 
refers to {\it levensbehoefte} (essentials of
life), {\it tarwebrood} refers to {\it tarwe} (wheat bread), but 
we would expect to be able to deal with 
most of these cases with a relatively small amount
of work.

The expectation that polysemy in the target lexicon would be avoided by
restricting it to a semantic subset was not entirely correct:
there were 57 words with more than one sense
in the combined subsets (out of a total number of 982).
But, as discussed in \cite{vossen93}, 
many of the sense distinctions involved are somewhat subtle.
The sort of polysemy usually
involved is exemplified by the LDOCE entry for {\it beer} where
three senses are given: the first is the alcoholic drink sense, the second
the corresponding conventional portion sense, and the third a {\it broadened} 
sense \cite{cope+bri95}, which covers 
non-alcoholic {\it ginger beer}, etc.
Although it is not particularly satisfactory to either rely entirely on manual
intervention or to
link all Dutch words which
can be translated as {\it beer} to all three senses, the best approach 
to this problem would be to provide a more satisfactory monolingual 
representation of sense distinctions.


\subsection{Spanish/English}

The Spanish experiment was significantly different from the Dutch in a number of
respects, perhaps most importantly in the nature of the bilingual dictionary 
used.  The Vox-Harrap's Esencial 
\cite{voxharraps}, the only one available, is 
relatively small, containing about 16000 entries.  By contrast, the
monolingual VOX dictionary \cite{vox}
used to generate the source subset contains almost
90000 entries.  This results in omissions from the bilingual 
dictionary being a far 
more serious problem than in the preceding experiment.
 

Another major difference was that, in order to allow for convenient and
flexible expansion of the heuristics to produce different types of tlinks in
different situations, an interactive environment was developed in which to
experiment with tlink construction.  The Tlink Generation Environment (TGE) 
was implemented using a production rule approach which had already been utilised 
in the SEISD system (Sistema de Extracci\'{o}n de Informaci\'{o}n
Sem\'{a}ntica de Diccionarios: \cite{ageno92})
for constructing monolingual LKB entries from dictionaries.
The core of the TGE is the production rule environment PRE, a rule-oriented 
general purpose interpreter adapted to natural language applications,
which offers a powerful rule application mechanism
allowing inheritance between rulesets and a choice of control strategies.
TGE is described in more detail in
\cite{ageno93}, \cite{ageno94} and \cite{sam94}.
In contrast with the work on Dutch, tlinks can be checked by the user
as they are being constructed.

The characterisation of tlink types used was slightly different from
the previous experiment.  Three main types were assumed: {\bf simple-tlinks},
{\bf phrasal-tlinks} and {\bf partial-tlinks}, but {\bf simple-tlinks} and
{\bf partial-tlinks} can be subdivided according to the TGE module 
or ruleset used to  
generate them.
\begin{description}
\item[simple-tlink] As in the previous experiment, these correspond to
mappings where there is a direct
mapping between single entries in the source and target subsets.
\begin{description}
\item[simple tlink module]
The mapping is generated from a word to word equivalence in the bilingual
dictionary.
\item[orthographic tlink module] 
This corresponds to the {\bf orth-tlink} type of the previous experiment.
\item[compound tlink module] 
This was applied where the English translation was made up of two or more orthographic
words, but nevertheless corresponded to a single LKB entry 
(e.g. milk\_shake\_L\_0\_0).
\end{description}
\item[phrasal-tlink]
These were distinguished from partial-tlinks and generated only when the phrase
could be completely analysed.  Appropriate rules have been developed for
English noun-noun compounds and for verb-particle constructions.
However there were very few cases where full analysis was possible
for the drinks subset considered here.
\item[partial-tlink] 
Partial-tlinks were generated when the
translation phrase in the bilingual dictionary could not be fully analysed, or
where there are gaps in the bilingual dictionary or the target subset.  (The
phrasal and compound tlinks mentioned in \S\ref{dutch} should be
considered as varieties of partial tlink.)  Three modules were
developed to handle particular subcases:
\begin{description}
\item[parent tlink module]
Many terms in the monolingual source subset were not found in the
bilingual dictionary, but their hyperonyms in the taxonomy had a clear translation.
For example, although {\it co\~{n}a} was not given in the bilingual dictionary, it 
could be linked to {\it cognac} in the English subset via a parent-link, 
because it was described in the Spanish monolingual dictionary using the genus term
{\it co\~{n}ac} which is translated as {\it cognac}.
\item[grandparent tlink module]
This is similar to the previous case, but applies when the direct parent was
also not translatable via the bilingual dictionary.  (This happened
quite frequently: the VOX monolingual 
is not a learners' dictionary and does not have a controlled definition
vocabulary, so relatively uncommon words are sometimes found in definitions
and the taxonomies tend to have more levels than those found in LDOCE.)
\item[general tlink module]
This applies when a phrase which cannot be fully analysed is given as a 
translation in the bilingual dictionary.  Like monolingual definitions, these can normally
be regarded as consisting of
a genus term and its modifiers, and the source entry can be
linked to the entry for the genus term.  The {\bf compound-tlink} and 
{\bf phrasal-tlink} types in the Dutch experiment essentially correspond to this case.
\end{description}
\end{description}

When using TGE for tlink generation, the
lexicon builder must accept or reject the
system's proposals.  This process can be carried on in various ways
depending on the TGE control strategy, the modules triggered by this
strategy, and the overall goals of the procedure. 
TGE
allows different modes of performance according to the intended goal. Each
mode is appropriate for different kinds of coverage and implies different
level of human intervention. It is possible, for instance, to apply all the
generation modules in order to select all the possible tlinks, 
allowing the user to select the appropriate one(s).
Another possibility is to rank the different rulesets and apply them in
order, presenting potential tlinks
one at a time to the user until one
is selected.  In
the former case the user must select zero or more entries from a list, in
the latter the choice is simply a yes/no question. 
There are also intermediate possibilities.
Different
tlink proposals can be ranked according to the LUCIFER appropriateness
measure.
However in the reported experiment 
the utility of this indicator was low, because of the lack of
training material caused by the restricted amount of polysemy in the 
semantically restricted subsets.

The task the user has to perform is thus
quite simple but requires a good knowledge
of both the source and target languages, although the system could provide
all the information needed.  Sometimes the
decision to be taken requires that the user look at complementary
information such as the source or target dictionary definitions, the source or
target lexical entries, the bilingual information and so on. The need for this
complementary information depends not only on the level of
fluency of the user but also
on the difficulty of the domain and the level of detail of the
lexicons involved.   
In the experiment, whose
results are given below, a rather conservative approach was taken, i.e.
all the modules looking for tlinks were activated and all the
generated tlinks acceptable in terms of translation and leading to
differentiable target lexical entries were accepted as tlinks.

The Spanish drink taxonomy contained 235 senses and had 5 levels.
In total 223 (95\%) of these senses were linked, often by multiple
tlinks.
In fact, the total number of tlinks resulting from the experiment
was considerably higher than the number of senses
(377 tlinks for the 223 linked lexical entries), 
although most
of these were partial links. 
The time taken by an expert lexicon builder to construct these tlinks 
was about three hours, 
i.e.~about 2.1 tlinks/minute or 1.2 senses/minute.  
The results classified by tlink type are summarised in Table~\ref{esp}.
\begin{table}
\caption{Spanish/English tlinks}
\label{esp}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}lllll}
\hline
type & module & number of tlinks & Spanish entries & English entries \\ \hline
simple-tlink & simple        & 41 & 26 & 31\\
             & compound      & 1  & 1  & 1\\
             & orthographic  & 13 & 13 & 13\\ \hline
             & total         & 55 (14.5\%) & &\\ \hline \hline
phrasal-tlink & phrasal &      2 (0.5\%)  & 1 & 3\\ \hline \hline
partial-tlink & parent       & 268 & 149  &15\\
              & grandparent  & 44   & 30 & 10\\
              & general      & 8    & 7 & 6\\ \hline
              & total        & 320 (85\%) & &\\ \hline
\end{tabular*}
\end{table}

Only 74 source entries (31.5\%) were found
in the bilingual dictionary and another 13 were linked by comparison of
orthography.
This result is explained by the great number of highly
specific lexical entries in the source subset, as is implied
by the difference of coverage between the two dictionaries and the
relatively high depth of the source taxonomy. 
The remaining 148 (63\%) lexical entries 
could only be linked partially with this bilingual dictionary,
even in the best case of no gaps in the monolingual English lexicon and
complete analysis of phrases used in translation.
In fact, the situation was worse than this, since
out of 74 translations, only 30 directly corresponded to target LKB
entries.

This experiment was carried out unidirectionally (unlike the Dutch trial)
using Spanish as a source and English as a target.  However it
resulted in 52 English senses out of the 192
in the target subset being linked (27\%). 
Of course these results must be viewed with caution.
Some of the English senses linked by TGE correspond to partial tlinks and
can, therefore, 
incorporate other information. In the next section some examples
of this are discussed.

\section{Discussion and evaluation}
\label{res}

These trials demonstrate that senses in existing monolingual lexical knowledge
bases of the sort developed in ACQUILEX
can be linked automatically or with little human intervention, 
with the aid of bilingual MRDs. 
Expressed in percentages of senses linked, the results vary from 60\%
(English food subset) to 95\% (Spanish drink subset). 
The quality of the links, that is, the percentage of simple rather than
partial tlinks, varies considerably depending on the
dictionaries.   We would expect to be able to increase the rate of
linking in the Dutch/English experiment to around the 95\% figure by
making use of parent tlinks as in the Spanish/English trials.
Of course the technique would be
useful with much lower linking rates,
because it automates work that would otherwise have to be done
manually.   

\mbox{}From our perspective these results are a 
success, since they demonstrate that much of the work involved in 
creating multilingual LKBs from monolingual LKBs could be automated,
despite the sense correspondence problem.  As a technique for building 
lexicons for MT, there are several qualifications.  Firstly, this work
requires existing semantically organised monolingual LKBs.  
Such LKBs are independently desirable, but although they can be
constructed semi-automatically from MRDs, the amount of human intervention
involved is significant (although less so for LDOCE than the other
dictionaries).  Secondly, although as indicated in \S\ref{tlink} some
complex cases of translation equivalence can be derived from simple
tlinks, there are many complex mappings which cannot be constructed
in this way and which cannot feasibly be constructed automatically
from MRDs.  The trials reported here all involve concrete nouns,
and we would expect abstract nouns, verbs and adjectives to be
considerably more difficult.  These results therefore show potential for
extending the vocabulary of an MT system, but we would not expect to
automatically construct the core bilingual lexicon.
Thirdly, the high linking rates involve the use of partial tlinks of
various types.  We believe these can be utilised in MT, but we return
to this point below, after discussing some other issues raised by these
experiments.

One limitation of the work described above is that the food and drink 
taxonomies 
do not contain all the senses which should be included in the respective
subsets, because lexicographers chose to classify them under another
genus term.\footnote
{Vossen and Copestake \cite{vossen+cope} discuss the 
classification found in LDOCE which results in {\it vegetable}, {\it cream},
{\it meat} and {\it spice} all being found outside the food taxonomy.} 
For example, no types of cheese were included in the Dutch {\it voedsel}
subset because {\it kaas} comes under {\it zuivelproduckt} 
(dairy product) in the taxonomy and not under {\it voedsel}.
Table~\ref{subsets} compares our best estimate for the size of the 
lexicons containing all senses corresponding to possible foods and drinks
for LDOCE and VanDale with the size of the taxonomies, and gives the
percentage of entries in each subset which were not linked by simple tlinks
because of classification problems.
\begin{table} 
\caption{Failure rate by subset for Dutch/English tlinks} 
\label{subsets}
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}llll}
\hline
& taxonomy size & full subset size & \% tlinks missed\\ \hline
LDOCE food & 260 & 594 & 40\\
LDOCE drink & 192 & 202 & 16\\
Van Dale food & 476 & 1190 & 18\\
Van Dale drink & 214 & 261 & 6\\ \hline
\end{tabular*}
\end{table}

It can thus be seen that, although
differences in classification were not the major source of gaps overall,
they were responsible for the poorer
linking rate in the food subsets in the Dutch experiments compared to the
drink subset.  The LDOCE classifications are more
systematic than those from Van Dale
in that a smaller percentage of senses are found outside
the taxonomies.  This probably reflects the restricting effect
the use of a core defining vocabulary in LDOCE had on the taxonomies.
But there were some examples of genuine conceptual mismatches between
the two subsets, for example, Dutch {\it drank} also included liquid
medicines and so on, such as {\it mondspoeling} ({\it mouthwash}) and
{\it purgeerdrank} ({\it laxative, purgative}).
It is thus clear that results would improve if we were in a position to run 
experiments over the entire dictionaries.
The type hierarchy in the LKB does not reflect the
taxonomies directly: for example {\it zuivelproduckt} would be classified
as having the type which covers foods and drinks, and so
this set of omissions 
would have been avoided if the subset had been defined in terms
of LKB types, rather than taxonomically.  Because of this, and because of the
polysemy problems mentioned previously, the strategy 
adopted in the Spanish experiment of
comparing LKB entries rather than derived dictionary entries is preferable
in the longer term.

We mentioned in \S\ref{tlink} that the tlink formalism was designed to be
symmetric, but it is clear from examination of the results that 
different tlinks are produced by going from Dutch to English via the VDE, than
from English to Dutch using the VED.  Clearly lexicographers are not
going to give infrequently used words or senses as translations if 
alternatives are available, though they have to list the obscure words if
the dictionary is intended to be comprehensive.  For example, 
both {\it cider} and {\it cyder} are found in the VED but we find {\it cider}
but not {\it cyder} given as a translation of {\it appelwijn}  in the VDE.
This effect partly accounts for the low percentage of linking of English senses
in the Spanish experiment which only used a Spanish-English bilingual
dictionary:  the less common English words were not used as translations and
were therefore not linked.
For practical purposes, it would obviously be better to prefer tlinks which have
the `right' directionality in translation, but since this is a preference,
not an absolute restriction, it does not affect our basic assumption of 
bidirectionality.  It would also be sensible to prefer the main translations
given in the VDE and the VED over the secondary translations.
In general, however, we regard lexical choice as an issue which is distinct
from acquisition and construction of the lexicons, since we see it as important
for domain independence and maximum flexibility
to give a range of translation possibilities.

It turns out that some senses which we should classify as translational
equivalents do not have compatible RQSs.  One example, discussed in detail by
Vossen \cite{vossen93}
is {\it Sauternes}, which has the following definitions in
LDOCE and Van Dale respectively.
\begin{description}
\item[LDOCE] a type of sweet gold-coloured French wine
\item[VanDale] Franse zoete, witte wijn, vooral bij vis gebruikt\\
French sweet, white wine, esp. used with fish
\end{description}
The English RQS has the feature {\sc colour} instantiated to
{\bf gold}, whereas for Dutch it is {\bf white}.
Now, intuitively, {\it Sauternes} is a clear case of a term that should be
translation equivalent between English and Dutch. 
It is a loan word in both languages, and
within the EU it has an imposed
definition: it refers to a certain type of wine from a particular region
and it would be illegal to use the term to label any other drink.  There
thus seems more possibility of a misunderstanding between speakers of British and 
American English than between Dutch and British English speakers
(since in the USA all sorts of sweet white wine are described as
{\it Sauternes}). 
The
discrepancy in the RQS could be described as a representational failure:
it would not be contradictory to refer to {\it Sauternes} as {\it a gold-coloured
white wine} since {\it white} is
a classification of wine which has a rather indirect relationship to perceived
colour (and the same is true of Dutch {\it wit}).  So the {\sc colour} feature 
of the Dutch RQS should perhaps not have been instantiated to {\bf white}.
This illustrates the difficulty of constructing an adequate
semantic representation: since we cannot expect to get this completely
correct, we have to accept that some minor discrepancies will 
occur.\footnote{The first named
author is more disturbed by {\it vooral bij vis gebruikt}: {\it esp. used 
with fish}, which, 
if it is not an aberration on the part of an individual lexicographer, 
indicates a significant difference between Dutch and English
tastes.}

In general, however, we would expect RQSs of translation-equivalent structures
to be unifiable, though not identical
since different lexicographers will choose to highlight different aspects
of a concept, and, in any case, our automatic analysis of the
definitions is sometimes incomplete.  We can thus transfer RQS information
between the monolingual concepts.  Although we could avoid errors due to mismatch
by using default rather than monotonic unification, it is probably better 
to signal the discrepancies and allow the user to resolve them manually,
as appropriate.  

We have not had the opportunity to investigate the use of derived tlinks 
in a full MT system.
Trujillo \cite{trujwp}
has developed an approach to MT using a bilingual
lexicon as the only transfer component: the formalism assumed is
similar to tlinks and the system is implemented using the ACQUILEX 
LKB framework.   Trujillo demonstrates the adequacy of his
representation for translation of locative prepositions, making use of
a semantic classification of nouns which is compatible with the
assumptions we have made about the RQS.
There are two main issues to consider with respect 
to the utilisation of tlinks in more established approaches to MT: 
whether the information encoded in the tlinks 
(mainly via the monolingual lexical entries)
is rich enough to support various MT approaches, and whether the formalism
allows the tlinks to be appropriately translated.
Full tlinks contain more information than is assumed
by most approaches to transfer based translation, as illustrated
by the bilingual lexical
entries given as in papers such as those by 
Estival {\it et al.} \cite{estivaletal}
and Alshawi {\it et al.} \cite{alshawietal}.  Formally, there is a close 
correspondance between the equivalence of variables in
tlinks and the use of 
transfer variables assumed in those approaches.  Furthermore, because
the tlink description abstracts away from details of the linguistic encoding
as much as possible, by expressing equivalences in terms of the monolingual lexicon,
it would be relatively easy to automatically transform at least
the simpler tlinks into the bilingual lexicon needed.  The main issue would be
the compatability of the syntactic representation, but this is not particularly
problematic for nouns.  Most of the semantic detail would be simply discarded.
It is more difficult to see whether tlinks have anything to offer knowledge based
approaches to MT, because, although the amount of detail in the noun 
representations appears at least comparable to those illustrated in 
Nirenburg \etal \cite{nir87},
for example, there is far less agreement about semantic ontologies
than syntactic categories. 

However we think the main issue, at least for nouns, is how a translation
system can make use of partial tlinks.
As we saw above, there were many cases where the only tlink that could be 
constructed was a partial-tlink of some type.  
In a few cases, this indicates a problem with the
methodology: for example, Spanish {\it zumo} 
could only be given a partial tlink to {\it drink} rather than {\it juice}, 
because {\it juice} was missing from the drink taxonomy in English.
But most cases
are indicative of gaps, either in coverage of the MRDs or of a real
lack of a direct translation into the target language.  In some cases the concepts
involved are `local', in that they denote
a regional product or speciality, for example.  
For example, {\it Pened\'{e}s}, {\it Rioja} and
{\it Malaga} are in the Spanish drink subset as types of wine from those
localities.  Many more types of gin are available in the Netherlands
than in Britain, so we find {\it dubbelgebeide} ({\it gin made with twice the 
normal quantity of juniper berries: prime quality gin}), {\it bessejenever} 
({\it blackcurrant gin}), {\it moutjenever} ({\it malt gin}) and so on.

Whether we find such words in a bilingual dictionary or not will depend on 
its size, but the translation given
is normally a phrase, which explains or paraphrases the concept and is therefore
not necessarily appropriate as a translation in a given context.
For example, \exnum{+1}{a} would be distinctly odd as part of a narrative and
either \exnum{+1}{b} or \exnum{+1}{c} would probably be better.
\begin{ex}
\begin{subex}
He went into the bar and ordered a gin made with twice the 
normal quantity of juniper berries.
\end{subex}
\begin{subex}
He went into the bar and ordered a gin.
\end{subex}
\begin{subex}
He went into the bar and ordered a {\it dubbelgebeide}.
\end{subex}
\end{ex}
Thus even good, wide-coverage bilingual dictionaries, such as the Van Dale ones,
do not always give translations that are usable.
The choice of food and drink as subsets 
means that the genuine mismatches we find are normally because of 
cultural localisation, rather than differences of conceptualisation
between two languages.  
But the same effect is observed with many other 
classes of noun (professions, institutions, transport, etc.), and it is 
essential to be able to deal with such cases in a broad coverage 
MT system (and in
machine-aided translation).

Our use of partial tlinks for such cases
is not just an artifact of our extraction methodology but
represents a first step in addressing this problem, which will arise for 
machine translation lexicons however they are generated.  As we said in the
introduction, 
the use of RQS can be seen as a way of partially decomposing
the semantic space for a particular class of senses.  Some such concepts
correspond to a single word (or established compound, etc.) in one language but
cannot be expressed (even approximately) without the use of a phrase in another.
Rather than hard-wiring a particular phrase into the bilingual lexicon, the
representation gives us the option of constructing a partial tlink, and 
transferring the extra information from the source monolingual entry
to the target representation, giving a 
feature structure which incorporates the additional semantic information which the
target generator can choose to express or not, as appropriate.
For example, the Spanish
{\it Pened\'{e}s} can be linked to a FS equivalent to
that for {\it wine} minus the orthography with the 
{\sc origin-area} feature in the RQS instantiated to {\bf Pened\'{e}s}.  
In this particular case, there is a lexical rule for English that a
place name
can also be used to refer to a characteristic product of that place.  Thus if
{\it Pened\'{e}s} is accessible to the English generator as a place name
the lexical rule will generate an underspecified representation which will
unify with the output of the tlink (ignoring orthography).\footnote{Note that the 
output of the lexical rule is underspecified, because we do not assume that
the lexicon contains information about what is produced in 
Pened\'{e}s.}
Thus, the generator
will find the string {\tt "}Pened\'{e}s{\tt "} as a possible realisation
of the semantics, even though it is not an explicit entry
in the target lexicon.  Alternatively the modifier might be ignored altogether
or a phrase {\it wine from Pened\'{e}s} might be
constructed.  
(Since the RQSs are derived by parsing the monolingual 
descriptions, we could in principle reverse this process, allowing the generator
to produce definition-like strings as one mode of operation.)

Of course, this requires that the target generator is capable of recognising
whether an underspecified translation is appropriate or not.  In the limit, this
is far beyond the capabilities of today's systems, since it would require
detailed knowledge of the author's intentions.   
But there are contexts where it is possible to think of automatically applicable
rules.  A bilingual restaurant menu, for example, should have a paraphrase rather
than a generic term or repetition of the source language word, because it is
appropriate for it to be maximally
informative.  In contrast, a translation into English of an article about
Spanish wine, for example, should refer to {\it Pened\'{e}s} rather than 
{\it wine from Pened\'{e}s} to avoid redundancy.
In general, we see partial tlinks as providing the information necessary to
make such choices possible as an alternative to hardwiring a particular 
translation.  This fits in naturally with the approach of translation
by negotiation
advocated by Kay \cite{verbmbook}: the generator can either accept
the partial information as adequate in the context, or attempt to construct
a target language representation which conveys as much of the extra information as 
is needed.
\mbox{}From the lexical viewpoint, the problem comes in attempting to encode the right
information in the partial interlingua, currently embodied by the RQS, to make
such flexibility possible, without sacrificing efficiency and directness in the 
(many) cases where a straightforward lexical transfer is appropriate.

\section{Conclusion}

The experiments reported have shown the feasibility of 
constructing useful fragments of multilingual lexicons from MRDs but have also
demonstrated the limitations of existing dictionaries.  
Although there are various improvements that we could make to the extraction
technology in order to avoid construction of spurious tlinks, there is no prospect
of constructing completely adequate lexicons fully automatically from MRDs, 
because the dictionaries themselves do not contain all the 
necessary information.
As in the monolingual case, MRDs are most usefully seen as tools to speed
production of the lexicon, or to extend a core lexicon,
not to automate lexicon construction entirely.
Similarly, linked LKBs have potential for machine-aided
translation, to give a human translator a possible
translation or choice of translations efficiently, omitting terms which
would appear in a bilingual dictionary
but which are syntactically or semantically inappropriate in that context.

There are several areas of further work, which we have started or
intend to carry out.  
In order to improve the sense-to-sense
mapping, a more satisfactory representation of polysemy in the monolingual 
lexicons is needed than treating 
each sense given by the lexicographer as fully distinct: some theoretical 
work on polysemy is described in \cite{cope+bri95}
and we expect this to lead to an improved
representation within the LKB.
Systematic translation mismatches are being investigated for 
Spanish and English \cite{mart+sol}.

Further experiments on tlink generation will be carried out on other
syntactic and semantic categories, but this requires the use of larger
monolingual lexicons.  Extension of the current type system to include 
adjectives and a finer-grained semantic classification for verbs (verbal
RQS) is currently being carried out within the ACQUILEX II project.
The definition of selectional restrictions in order to generate phrasal tlinks
and the extension of tlinks from lexical entries to lexical rules is currently 
under research. Special attention has been paid to the relationships between
verbal diathesis rules in Spanish-English.

One final point is that, to some extent, the inadequacies of bilingual
dictionaries as sources for MT
lexicons, also reflect inadequacies in their intended use
for humans.  For example, the Deutsch-Englisch
Langenscheidts Taschenw\"{o}rterb\"{u}cher \cite{langen},
contains the entry:
\begin{quote}
{\bf Kieme} {\it f} gill.
\end{quote}
with no indication of which sense of {\it gill} is meant.  To a British English 
speaker, at least, this could plausibly refer to a slit which is
part of a fish or a mushroom, a measure of volume for liquids, or a fast running
stream.
The sort of techniques which we have described here for constructing tlinks
could be adapted to automatically indicate the possibility
of ambiguity to a lexicographer who creates such an entry, 
prompting them to add a disambiguating word or label.
There are obvious advantages in both monolingual and bilingual cases
for incorporating a semantic coding scheme to be instantiated by lexicographers,
since this would allow consistency and coherence checks of this sort to
be carried out, and allow systematic sense extensions and translation mismatches
to be dealt with consistently.  
Such techniques are actively under investigation by dictionary publishers
in the context of the ACQUILEX II project with the aim not only of improving
printed dictionaries, but of creating lexical databases which can also be
exploited for NLP and MT. 
The long-term utility of the work reported here almost
certainly rests on utilising the insights gained to help determine the
content and format of these lexical databases so that many of the
problems identified are resolved when they are
constructed.

\acknowledgments
This work was carried out under the ACQUILEX projects (3030 and 7315)
funded by the EC under the Esprit basic research program.
Francesc Ribas was 
supported by a grant from the Departament d'Ensenyament 
of Generalitat de Catalunya, 91-DOCG-1491.
German Rigau was supported by a grant from the  Ministerio de Educaci\'{o}n 
y Ciencia, 92-BOE-16392.
We are grateful to the publishers, Van Dale, Biblograf and 
Longman for giving us permission to use their dictionaries and to
Arturo Trujillo and two anonymous referees for their comments on this paper.

\appendix{}
For reasons of space the tlinks shown are limited to
those with source entries
beginning with a or b,
except where this would have excluded all members of a class,
in which case the first one or two entries of the class are shown.
\subsection*{Dutch drank to English drink}
\paragraph{Simple tlinks}\\
{\small
ABSINT\_V\_0\_1/ ABSINTH\_L\_0\_1\\
ADVOCAAT\_V\_0\_3/ EGGNOG\_L\_0\_0\\
ADVOCAAT\_V\_0\_3/ FLIP\_L\_2\_2\\
ALCOHOL\_V\_0\_2/ ALCOHOL\_L\_0\_2\\
ALCOHOL\_V\_0\_2/ DRINK\_L\_2\_1\\
ALCOHOL\_V\_0\_2/ SPIRIT\_L\_1\_12\\
APPELWIJN\_V\_0\_1/ CIDER\_L\_0\_1\\
APPELWIJN\_V\_0\_1/ CIDER\_L\_0\_3\\
ARAK\_V\_0\_1/ ARRACK\_L\_0\_0\\
BEAUJOLAIS\_V\_0\_1/ BEAUJOLAIS\_L\_0\_0\\
BIER\_V\_0\_1/ ALE\_L\_0\_0\\
BIER\_V\_0\_1/ BEER\_L\_0\_1\\
BIER\_V\_0\_1/ BEER\_L\_0\_2\\
BIER\_V\_0\_1/ BEER\_L\_0\_3\\
BISSCHOPWIJN\_V\_0\_1/ WINE\_L\_1\_1\\
BISSCHOPWIJN\_V\_0\_1/ WINE\_L\_1\_2\\
BOURBON\_V\_0\_1/ BOURBON\_L\_0\_0\\
BOURGOGNE\_V\_0\_1/ BURGUNDY\_L\_0\_0\\
BOWL\_V\_0\_2/ CUP\_L\_1\_6\\
BOWL\_V\_0\_2/ PUNCH\_L\_4\_0\\
BRANDEWIJN\_V\_0\_1/ BRANDY\_L\_1\_1\\
BRANDEWIJN\_V\_0\_1/ BRANDY\_L\_1\_2
}

\paragraph{Phrasal tlinks}\\
{\small
BESSENJENEVER\_V\_0\_1/ GIN\_L\_2\_0 (BLACKCURRANT)\\
BESSENWIJN\_V\_0\_1/ WINE\_L\_1\_2 (CURRANT)\\
BESSENWIJN\_V\_0\_1/ WINE\_L\_1\_1 (CURRANT)\\
BESSENWIJN\_V\_0\_1/ WINE\_L\_1\_2 (RED CURRANT)\\
BESSENWIJN\_V\_0\_1/ WINE\_L\_1\_1 (RED CURRANT)\\
BITTER\_V\_0\_1/ GIN\_L\_2\_0 (AND BITTERS)\\
BLOEDWIJN\_V\_0\_1/ WINE\_L\_1\_2 (TONIC)\\
BLOEDWIJN\_V\_0\_1/ WINE\_L\_1\_1 (TONIC)\\
BOLS\_V\_0\_1/ GIN\_L\_2\_0 (DUTCH)
}

\paragraph{Orthographic tlinks}\\
{\small
BITTER\_V\_0\_1/ BITTER\_L\_3\_0\\
BRANDY\_V\_0\_1/ BRANDY\_L\_1\_2\\
BRANDY\_V\_0\_1/ BRANDY\_L\_1\_1
}

\subsection*{Tlinks derived from English drink to  Dutch drank}
\paragraph{Simple Tlinks}\\
{\small
ABSINTH\_L\_0\_1/ ABSINT\_V\_0\_1\\
ALCOHOL\_L\_0\_2/ ALCOHOL\_V\_0\_2\\
ALE\_L\_0\_0/ BIER\_V\_0\_1\\
AQUA\_VITAE\_L\_0\_0/ ALCOHOL\_V\_0\_2\\
AQUA\_VITAE\_L\_0\_0/ BRANDEWIJN\_V\_0\_1\\
ARRACK\_L\_0\_0/ ARAK\_V\_0\_1\\
BEAUJOLAIS\_L\_0\_0/ BEAUJOLAIS\_V\_0\_1\\
BEER\_L\_0\_1/ BIER\_V\_0\_1\\
BEER\_L\_0\_2/ BIER\_V\_0\_1\\
BEER\_L\_0\_3/ BIER\_V\_0\_1\\
BITTER\_L\_3\_0/ BITTER\_V\_0\_1\\
BOCK\_L\_0\_0/ BOCKBIER\_V\_0\_1\\
BOOZE\_L\_2\_1/ DRANK\_V\_0\_1\\
BOOZE\_L\_2\_1/ DRANK\_V\_0\_2\\
BOOZE\_L\_2\_1/ STERKEDRANK\_V\_0\_1\\
BOTTLE\_L\_1\_3/ DRANK\_V\_0\_1\\
BOTTLE\_L\_1\_3/ DRANK\_V\_0\_2\\
BOURBON\_L\_0\_0/ BOURBON\_V\_0\_1\\
BRANDY\_L\_1\_1/ BRANDEWIJN\_V\_0\_1\\
BRANDY\_L\_1\_1/ COGNAC\_V\_0\_1\\
BRANDY\_L\_1\_2/ BRANDEWIJN\_V\_0\_1\\
BRANDY\_L\_1\_2/ COGNAC\_V\_0\_1\\
BURGUNDY\_L\_0\_0/ BOURGOGNE\_V\_0\_1
}
\paragraph{Phrasal Tlinks}\\
{\small
BLACK\_AND\_TAN\_L\_0\_3/ BITTER\_V\_0\_1 (MENGSEL V BIER EN STOUT)\\
BLACK\_AND\_TAN\_L\_0\_3/ BIER\_V\_0\_1 (MENGSEL V BITTER EN STOUT)\\
BLACK\_AND\_TAN\_L\_0\_3/ STOUT\_V\_0\_1 (MENGSEL V BITTER BIER EN)
}
\paragraph{Compound Tlinks}\\
{\small
ANIS\_L\_0\_0/ LIKEUR\_V\_0\_1 (ANIJS)\\
APPLEJACK\_L\_0\_0/ WIJN\_V\_0\_3 (APPELBRANDE)\\
APPLEJACK\_L\_0\_0/ WIJN\_V\_0\_1 (APPELBRANDE)\\
APPLEJACK\_L\_0\_0/ BRANDEWIJN\_V\_0\_1 (APPEL)\\
BARLEY\_WINE\_L\_0\_0/ WIJN\_V\_0\_3 (GERSTE)\\
BARLEY\_WINE\_L\_0\_0/ WIJN\_V\_0\_1 (GERSTE)
}

\paragraph{Orthographic Tlinks}\\
{\small
COLA\_L\_0\_0/ COLA\_V\_0\_1
}

\subsection*{Spanish bebida to English drink}

\paragraph{Simple tlink module}\\
{\small
AGUARDIENTE\_X\_I\_1 /  LIQUOR\_L\_0\_1 \\
AGUARDIENTE\_X\_I\_1 /  LIQUOR\_L\_0\_2 \\
AGUARDIENTE\_X\_I\_1 /  BRANDY\_L\_1\_1 \\
AGUARDIENTE\_X\_I\_1 /  BRANDY\_L\_1\_2 \\
AJENJO\_X\_I\_2 /  ABSINTH\_L\_0\_1 \\
BEBEDIZO\_X\_I\_2 /  POTION\_L\_0\_0 \\
BEBEDIZO\_X\_I\_3 /  POTION\_L\_0\_0 \\
BEBIDA\_X\_I\_3 /  DRINK\_L\_2\_1 \\
BRANDI\_X\_I\_1 /  BRANDY\_L\_1\_1 \\
BRANDI\_X\_I\_1 /  BRANDY\_L\_1\_2 
}


\paragraph{Compound tlink module}\\
{\small
BATIDO\_X\_I\_5 /  MILK\_SHAKE\_L\_0\_0
}

\paragraph{Orthographic tlink module}\\
{\small
ALE\_X\_I\_1 /  ALE\_L\_0\_0 \\
BOURBON\_X\_I\_1 /  BOURBON\_L\_0\_0 
}

\paragraph{Parent tlink module}\\
{\small
ABSENTA\_X\_I\_1 /  ABSINTH\_L\_0\_1\\
ABSENTA\_X\_I\_1 /  DRINK\_L\_2\_1\\
ADIAFA\_X\_I\_1 /  REFRESHMENT\_L\_0\_2\\
AGASAJO\_X\_I\_3 /  REFRESHMENT\_L\_0\_2\\
AGUACHIRLE\_X\_I\_1 /  WINE\_L\_1\_1\\
AGUACHIRLE\_X\_I\_1 /  WINE\_L\_1\_2\\
AGUAPI\'{E}\_X\_I\_1 /  WINE\_L\_1\_1\\
AGUAPI\'{E}\_X\_I\_1 /  WINE\_L\_1\_2\\
AHUMADO\_X\_I\_4 /  WINE\_L\_1\_1\\
AHUMADO\_X\_I\_4 /  WINE\_L\_1\_2\\
ALBARI\~{N}O\_X\_I\_1 /  WINE\_L\_1\_1\\
ALBARI\~{N}O\_X\_I\_1 /  WINE\_L\_1\_2\\
ALICANTE\_X\_I\_3 /  WINE\_L\_1\_1\\
ALICANTE\_X\_I\_3 /  WINE\_L\_1\_2\\
ALMENDRADA\_X\_I\_1 /  DRINK\_L\_2\_1\\
ALOQUE\_X\_I\_2 /  WINE\_L\_1\_1\\
ALOQUE\_X\_I\_2 /  WINE\_L\_1\_2\\
ALQUERMES\_X\_I\_2 /  LIQUOR\_L\_0\_1\\
ALQUERMES\_X\_I\_2 /  LIQUOR\_L\_0\_2\\
AMONTILLADO\_X\_I\_1 /  WINE\_L\_1\_1\\
AMONTILLADO\_X\_I\_1 /  WINE\_L\_1\_2\\
AMOROSO\_X\_I\_5 /  WINE\_L\_1\_1\\
AMOROSO\_X\_I\_5 /  WINE\_L\_1\_2\\
ANG\'{E}LICA\_X\_I\_5 /  DRINK\_L\_2\_1\\
ANISADO\_X\_I\_2 /  BRANDY\_L\_1\_1\\
ANISADO\_X\_I\_2 /  BRANDY\_L\_1\_2\\
ANISADO\_X\_I\_2 /  LIQUOR\_L\_0\_1\\
ANISADO\_X\_I\_2 /  LIQUOR\_L\_0\_2\\
ANISETE\_X\_I\_1 /  LIQUOR\_L\_0\_1\\
ANISETE\_X\_I\_1 /  LIQUOR\_L\_0\_2\\
A\~{N}EJO\_X\_I\_3 /  WINE\_L\_1\_1\\
A\~{N}EJO\_X\_I\_3 /  WINE\_L\_1\_2\\
APERITIVO\_X\_I\_2 /  DRINK\_L\_2\_1\\
ARLEQU\'{I}N\_X\_I\_5 /  DRINK\_L\_2\_1\\
ARMA\~{N}AC\_X\_I\_1 /  BRANDY\_L\_1\_1\\
ARMA\~{N}AC\_X\_I\_1 /  BRANDY\_L\_1\_2\\
ARMA\~{N}AC\_X\_I\_1 /  LIQUOR\_L\_0\_1\\
ARMA\~{N}AC\_X\_I\_1 /  LIQUOR\_L\_0\_2\\
ATOLE\_X\_I\_1 /  DRINK\_L\_2\_1\\
AURORA\_X\_I\_6 /  DRINK\_L\_2\_1\\
AVENATE\_X\_I\_1 /  DRINK\_L\_2\_1\\
BALARRASA\_X\_I\_1 /  BRANDY\_L\_1\_1\\
BALARRASA\_X\_I\_1 /  BRANDY\_L\_1\_2\\
BALARRASA\_X\_I\_1 /  LIQUOR\_L\_0\_1\\
BALARRASA\_X\_I\_1 /  LIQUOR\_L\_0\_2\\
B\'{A}SIG\_X\_I\_1 /  LIQUOR\_L\_0\_1\\
B\'{A}SIG\_X\_I\_1 /  LIQUOR\_L\_0\_2\\
BEBER\_X\_I\_1 /  DRINK\_L\_2\_1:\\
BEBIDO\_X\_I\_2 /  DRINK\_L\_2\_1\\
BEBIENDA\_X\_I\_1 /  DRINK\_L\_2\_1\\
BENEDICTINO\_X\_I\_4 /  LIQUOR\_L\_0\_1\\
BENEDICTINO\_X\_I\_4 /  LIQUOR\_L\_0\_2\\
BREBAJE\_X\_I\_1 /  DRINK\_L\_2\_1\\
B\'{I}TER\_X\_I\_1 /  DRINK\_L\_2\_1
}

\paragraph{Grandparent tlink module}\\
{\small
ACETOMIEL\_X\_I\_1 /  DRINK\_L\_2\_1\\
AN\'{I}S\_X\_I\_5 /  BRANDY\_L\_1\_1\\
AN\'{I}S\_X\_I\_5 /  BRANDY\_L\_1\_2\\
AN\'{I}S\_X\_I\_5 /  LIQUOR\_L\_0\_1\\
AN\'{I}S\_X\_I\_5 /  LIQUOR\_L\_0\_2\\
ARLEQU\'{I}N\_X\_I\_5 /  REFRESHMENT\_L\_0\_2\\
ARROPE\_X\_I\_3 /  DRINK\_L\_2\_1\\
BREBAJO\_X\_I\_1 /  DRINK\_L\_2\_1\\
B\'{I}TTER\_X\_I\_1 /  DRINK\_L\_2\_1
}


\paragraph{General tlink module}\\
{\small
ESTOMACAL\_X\_I\_2 /  LIQUEUR\_L\_0\_0\\
FILTRO\_X\_II\_2 /  POTION\_L\_0\_0
}

\paragraph{Phrasal noun tlink module}\\
{\small
CLARETE\_X\_I\_1 /  CLARET\_L\_1\_0+WINE\_L\_1\_1 \\
CLARETE\_X\_I\_1 /  CLARET\_L\_1\_0+WINE\_L\_1\_2
}

\newpage
\bibliographystyle{plain}

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\newcommand{\booknoauthor}[3]{#1 (#3) {\it #2,}}
\newcommand{\forthedbook}[4]{#1 (eds.) (#4, forthcoming) {\it #2,} #3.}
\newcommand{\unpub}[4]{#1 (#4) {\it #2,} #3.}
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\newcommand{\bookartnopp}[6]{#1 (#6) `#2' in #4 (eds.), {\it #3,} #5.}
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{\it #3,} #5.}
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\newcommand{\procart}[6]{#1 (#6) `#2', {\it Proceedings of the
#3,} #4, pp.~#5.}
\newcommand{\forthprocart}[5]{#1 (#5) `#2', 
to appear in {\it Proceedings of the #3,} #4}
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#3,} #4.}
\newcommand{\forthjournart}[4]{#1 (#4) `#2', {\it #3}}


\begin{references}
\bibitem{ageno92}
\procart{Ageno, A., I. Castellon, G. Rigau, H. Rodriguez, M.F.~Verdejo, 
M.A.~Marti and  M.~Taule}
{SEISD: An Environment for Extraction of Semantic Information from
On-Line Dictionaries}
{3rd Conference on Applied Natural Language Processing (ANLP-92)} 
{Trento, Italy}
{253--255}
{1992}

\bibitem{ageno93}
\unpub{Ageno, A., F. Ribas, G. Rigau, H. Rodriguez, F.Verdejo}
{TGE: Tlinks Generation Environment} 
{ACQUILEX II Working paper 8}
{1993}

\bibitem{ageno94}
\procartnopp{Ageno, A., F. Ribas, G. Rigau, H. Rodriguez, A. Samiotou}
{TGE: Tlinks Generation Environment} 
{15th International Congress on Computational Linguistics (COLING 94)}
{Kyoto, Japan}
{1994}

\bibitem{alshawietal}
\procart{Alshawi, H., D. Carter, M. Rayner and B. Gamb\"ack}
    {Translation by quasi logical form transfer}
    {29th Annual Meeting of the Association for Computational
Linguistics (ACL-91)}
    {Berkeley, California}
    {161--168}
    {1991}

\bibitem{vox}
\booknoauthor{Biblograf S.A.(publisher)}
{Diccionario general ilustrado de la Lengua Espa\~{n}ola VOX}
{Biblograf, S.A.: Barcelona}
{1987}

\bibitem{voxharraps}
\booknoauthor{Biblograf S.A. (publisher)}
{VOX Harraps Diccionario Esencial:  Ing\'{e}s-Espa\~{n}ol 
Espa\~{n}ol-Ingl\'{e}s. Second edition.}
{Biblograf, S.A.: Barcelona}
{1992}

\bibitem{bog+brisc}
\book{Boguraev, B. and ~E.~J.~Briscoe (eds)}
   {Computational lexicography for natural language processing}
   {Longman, London}
   {1989}

\bibitem{brisc91}
\bookart{Briscoe, E. J.}
    {Lexical issues in natural language processing}
    {39--68}
    {Natural language and speech}
    {E. Klein and F. Veltman}
    {Springer-Verlag}
    {1991}


\bibitem{calz}
\bookart{Calzolari, N.}
     {Acquiring and representing semantic information in a
      lexical knowledge base}
      {235--244}
     {Lexical Semantics and Knowledge Representation}
     {J.~Pustejovsky and S.~Bergler}
     {Lecture Notes in AI: 627, Springer-Verlag, Berlin}
     {1992}




\bibitem{carp}
\book{Carpenter, R.}
    {The Logic of Typed Feature Structures}
    {Cambridge University Press, Tracts in Theoretical Computer Science}
    {1992}


\bibitem{jac+grover}
\bookart{Carroll, J. and C. Grover}
   {The derivation of a large computational lexicon for English from LDOCE}
   {117--134}
   {Computational lexicography for natural language processing}
   {B.~Boguraev and E.~J. Briscoe}
   {Longman, London}
   {1989}

\bibitem{cope90}
\procart{Copestake, A.}
     {An approach to building the hierarchical element of 
      a lexical knowledge base from a machine readable dictionary}
     {First International Workshop on
      Inheritance in Natural Language Processing}
     {Tilburg, The Netherlands}
     {19--29}
     {1990}

\bibitem{cope92}
\procart{Copestake, A.}
{The ACQUILEX LKB: representation issues in semi-automatic
acquisition of large lexicons}
{3rd Conference on Applied Natural Language Processing (ANLP-92)}
{Trento, Italy}
{88--96}
{1992}


\bibitem{cope93}
\unpub{Copestake, A.}
{The Compleat LKB}
{University of Cambridge Computer Laboratory Technical Report No. 316}
{1993}

\bibitem{cope+bri95}
\journart{Copestake, A. and E.J.~Briscoe}
	{Semi-Productive Polysemy and Sense Extension}
	{Journal of Semantics}
        {12}
        {15--67}
	{1995}

\bibitem{cope+al92}
\bookart{Copestake, A., B.~Jones, A.~Sanfilippo, H.~Rodriguez, P.~Vossen,
S.~Montemagni and E.~Marinai}
{Multilingual lexical representation}
{117--129}
{The (other) Cambridge ACQUILEX papers}
{A.~Sanfilippo}
{University of Cambridge Computer Laboratory. Technical Report No. 253}
{1992}

\bibitem{cope+san}
\procartnopp{Copestake, A. and A.~Sanfilippo}
     {Multilingual lexical representation}
     {AAAI Spring Symposium on Building lexicons for machine translation}
     {Stanford, CA}
     {1993}


\bibitem{dowty}
\journart{Dowty,  D.}
        {Thematic proto-roles and argument selection }
	{Language}
        {67} 
        {547--619}
        {1991}


\bibitem{estivaletal}
\procart{Estival, D., A.~Ballim, G.~Russell and S.~Warwick}
    {A syntax and semantics for feature structure transfer}
    {3rd International Conference on theoretical and methodological
issues in MT of NLs (TMI-90)}
    {Austin,Texas}
    {131--143}
    {1990}

\bibitem{helm}
\unpub{Helmreich, S., L.~Guthrie and Y.~Wilks}
        {The Use of Machine Readable Dictionaries in the Pangloss
Project}
        {Paper presented at the AAAI Spring Symposium on Building
Lexicons for Machine Translation, Stanford University}
        {1993}

\bibitem{hutch+som}
\book{Hutchins, W.J. and H.L. Somers}
     {An Introduction to Machine Translation}
     {Academic Press, London}
     {1992}



\bibitem{kaplanetal}
\procart{Kaplan, R., K.~Netter, J.~Wedekind and A.~Zaenen}
	{Translation by Structural Correspondences}
	{4th Conference of the European Chapter of
	the Association for Computational Linguistics (EACL-89)}
	{Manchester, UK}
	{272--281}
	{1989}

\bibitem{verbmbook}
\book{Kay, M., J-M. Gawron and P.~Norvig}
     {Verbmobil: A translation system for face-to-face dialog}
     {CSLI Lecture Notes, No. 33: University of Chicago Press}
     {1994}

\bibitem{klavans}
\procartnopp{Klavans, J.L.}
     {COMPLEX: A Computational Lexicon for Natural Language Systems}
     {12th International Conference on Computational Linguistics
(Coling-88)}
     {Budapest, Hungary}
     {1988}

\bibitem{klavans90}
\procartnopp{Klavans, J.L., M.S.~Chodorow and N.~Wacholder}
     {From Dictionary to Knowledge Base via Taxonomy}
     {Sixth Conference of the University of Waterloo
Centre for the New Oxford English Dictionary and Text Research:
Electronic Text Research}
     {University of Waterloo, Canada}
     {1990}



\bibitem{levin}
\book{Levin,  B.}
{English Verb Classes and Alternations}
{Chicago University Press, Chicago}
{1993}


\bibitem{mart+sol}
\procartnopp{Marti, M.A. and C. Soler}
{Dealing with lexical mismatches}
{Euralex 94}
{Amsterdam, The Netherlands}
{1994}

\bibitem{ved}
\book{Martin, W. and G.A.J. Tops}
{Groot Woordenboek Engels-Nederlands}
{Van Dale Lexicografie: Utrecht, The Netherlands}
{1984}

\bibitem{vde}
\book{Martin, W. and G.A.J. Tops}
{Groot Woordenboek Nederlands-Engels}
{Van Dale Lexicografie: Utrecht, The Netherlands}
{1986}

\bibitem{langen}
\book{Messinger, H. and G.~T\"{u}rck}
{Deutsch-Englisch
Langenscheidts Taschenw\"{o}rterb\"{u}cher}
{Langenscheidt, Berlin}
{1990}

\bibitem{neffetal}
\unpub{Neff, M., B. Blaser, J-M Lange, H. Lehmann and I.~Dominguez}
        {Get it where you can: Acquiring and Maintaining Bilingual
Lexicons for Machine Translation}
        {Paper presented at the AAAI Spring Symposium on Building
Lexicons for Machine Translation, Stanford University}
        {1993}

\bibitem{neff}
\procartnopp{Neff, M. and M. McCord}
   {Acquiring Lexical Data from Machine-Readable Dictionary Resources
for Machine Translation}
   {Third International Conference on Theoretical and Methodological
Issues in Machine Translation of Natural Languages (TMI-90)}
   {Austin, Texas}
   {1990}

\bibitem{nir87}
\bookart{Nirenburg, S., V. Raskin and A. B. Tucker}
        {The Structure of Interlingua in TRANSLATOR}
        {90--113}
        {Machine Translation: Theoretical and Methodological Issues}
        {S.~Nirenburg}
        {Cambridge University Press, Cambridge, England}
        {1987}

\bibitem{nir+rask}
\journart{Nirenburg, S. and V. Raskin}
        {The Subworld Concept Lexicon and the Lexicon Management
System}
        {Computational Linguistics}
        {13(3-4)}
        {276--289}
        {1988}

\bibitem{ldoce}
\book{Procter, P. (ed)}
    {Longman Dictionary of Contemporary English}
    {Longman, London}
    {1978}

\bibitem{pust91}
\journart{Pustejovsky, J.}
    {The generative lexicon}
    {Computational Linguistics}
    {17(4)}
    {409--441}
    {1991}

\bibitem{sam93}
\unpub{Samiotou, A.}
{Performance of Cross-linguistic Equivalence Relations: 
A Lexicon-based approach}
{MSc Dissertation, Centre for Computational
Linguistics, UMIST, England}
{1993}

\bibitem{sam94}
\unpub{Samiotou, A., I. Castellon, F. Ribas, G. Rigau}
{Translation Equivalence via Lexicon: A Study on Tlinks} 
{ACQUILEX II Working paper 25}
{1994}

\bibitem{sanvsem}
\bookart{Sanfilippo, A.}
    {LKB encoding of lexical knowledge from machine-readable dictionaries}
    {190--222}
    {Inheritance, defaults and the lexicon}
    {E.~J.~Briscoe, A.~Copestake and V.~de~Paiva}
    {Cambridge University Press, Cambridge, England}
    {1993}


\bibitem{sanetal}
\procartnopp{Sanfilippo, A., E. J. Briscoe, A. Copestake,
M. A. Marti and A. Alonge}
{Translation equivalence and lexicalization in the ACQUILEX LKB}
{4th International Conference on Theoretical and 
Methodological Issues in Machine Translation (TMI-92)}
{Montreal, Canada}
{1992}


\bibitem{san+poz}
\procart{Sanfilippo, A. and V. Poznanski}
{The Acquisition of Lexical Knowledge from Combined Machine-Readable
Dictionary Sources}
{3rd Conference on Applied Natural Language Processing (ANLP-92)}
{Trento, Italy}
{80--88}
{1992}

\bibitem{shieber}
\book{Shieber, S.~M.}
{An Introduction to Unification-Based Approaches to Grammar}
{CSLI Lecture Notes 4, Stanford CA}
{1986}

\bibitem{vand}
\book{van Sterkenburg P. and W.J.J. Pijnenburg}
{Groot woordenboek van hedendaags Nederlands}
{Van Dale Lexicografie: Utrecht, The Netherlands}
{1984}

\bibitem{sutcliffe}
\unpub{Sutcliffe, R.}
      {Using Distributed Patterns as Language Independent Lexical Representations}
        {Paper presented at the AAAI Spring Symposium on Building
Lexicons for Machine Translation, Stanford University}
        {1993}


\bibitem{talmy85}
\bookartnopp{Talmy, L.}
    {Lexicalization patterns}
    {Language Typology and Syntactic Description}
    {T.~Shopen}
    {Cambridge University Press}
    {1985}


\bibitem{trujwp}
\unpub{Trujillo, I.A.}
      {Machine Translation with the ACQUILEX LKB}
      {ACQUILEX II Working Paper}
      {1995}

\bibitem{vossen90}
\unpub{Vossen, P.}
    {A Parser-Grammar for the Meaning Descriptions of LDOCE}
    {Links Project Technical Report 300-169-007, Amsterdam University}
    {1990}

\bibitem{vossen91}
\unpub{Vossen, P.}
{Converting Data from a Lexical Database
to a Knowledge Base}
{Acquilex working paper 27: University of Amsterdam}
{1991}

\bibitem{vossen92}
\bookartnopp{Vossen, P.}
{The automatic construction of a knowledge base from dictionaries: a
combination of techniques}
{Euralex '92 Proceedings I-II} 
{H.~Tommola, K.~Tarantola, T.~Salmin~Tolonen, J.~Schop}
{Tampere, Finland}
{1992}

\bibitem{vossen93}
\unpub{Vossen, P.}
{Extracting equivalence relations for a multilingual
Lexical knowledge base}  
{Acquilex 2 working paper No.~14: University of Amsterdam}
{1993}

\bibitem{vossen+cope}
\bookart{Vossen, P. and A.~Copestake}
    {Untangling definition structure into knowledge representation}
    {246--274}
    {Defaults, Inheritance and the Lexicon}
    {E.~J.~Briscoe, A.~Copestake and V.~de~Paiva}
    {Cambridge University Press, Cambridge, England}
    {1993}

\bibitem{vossenetal89}
\bookart{Vossen, P., W. Meijs and M. Broeder}
     {Meaning and structure in dictionary definitions}
     {171--192}
     {Computational Lexicography for Natural Language Processing}
     {B.~Boguraev and E.J.~Briscoe}
     {Longman, London}
     {1989}

\bibitem{walk+ams}
\bookart{Walker, D. and R. Amsler} 
          {The use of machine-readable dictionaries in sublanguage analysis} 
          {69--83} 
          {Analyzing Language in Restricted Domains} 
          {R.~Grishman and R.~Kittredge}  
          {Lawrence Erlbaum Associates, Hillsdale, New Jersey} 
          {1986}


\bibitem{whitelock}
\procartnopp{Whitelock, P.}
     {Shake-and-bake translation}
     {14th International Conference on Computational Linguistics (COLING-92)}
     {Nantes, France}
     {1992}


\bibitem{wilks}
\bookart{Wilks, Y., D. Fass, C-M. Guo, J.~McDonald, T.~Plate and B.~Slator}
   {A tractable machine dictionary as a resource for computational semantics}
   {193--231}
   {Computational lexicography for natural language processing}
   {B.~Boguraev and E.~J.~Briscoe}
   {Longman, London}
   {1989}

\bibitem{zajac}
\procart{Zajac, R.}
    {A transfer model using a typed feature structure rewriting system
with inheritance}
    {27th Annual Meeting of the Association for Computational
Linguistics (ACL-89)}
    {Vancouver, BC}
    {1--7}
    {1989}

\bibitem{inl}
\bookart{Zeevat, H.,  E. Klein  and J. Calder}
{An introduction  to unification  categorial  grammar}
{195--222}
{Categorial grammar, unification grammar, and parsing:
working papers in cognitive  science,  Volume 1}
{N.~Haddock, E.~Klein and G.~Morrill}
{Centre for Cognitive Science, University of Edinburgh}
{1987} 


\end{references}
(ACQUILEX~II Working Papers are available from cide@cup.cam.ac.uk)

\end{article}
\end{document}





