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\begin{document}
\thispagestyle{empty}
\vspace*{2in}
\begin{center}
{\Large \bf 
Multilingual lexical representation}\\
\smallskip
Ann Copestake, Bernie Jones, Antonio Sanfilippo\\[0.1in]
University of Cambridge Computer Laboratory\\
New Museums Site, Pembroke Street, Cambridge, CB2 3QG, UK\\[0.1in]
Horacio Rodr\'{\i}guez\\[0.1in]
Dept. Llenguatges i Sistemes Informatics, 
Universitat Polit\`{e}cnica de Cataluyna\\
PAU GARGALLO 5, 08028 Barcelona, Spain\\[0.1in] 
Piek Vossen\\[0.1in]
Department of English, University of Amsterdam \\
Spuistraat 210, 1012 VT Amsterdam, The Netherlands\\[0.1in]
Simonetta Montemagni, Elisabetta Marinai\\[0.1in]
Instituto di Linguistica Computazionale
Via della Faggiola 32, I-56100, Pisa, Italy\\
\smallskip
January 1992\\
\bigskip
ESPRIT BRA-3030 ACQUILEX Working Paper No. 043
\end{center}
\newpage
\setcounter{page}{1}

\setlength{\unitlength}{1mm}
\begin{center}
{\Large {\bf Multilingual lexical representation}}\\[0.1in]
{\large Ann Copestake, Bernie Jones, Antonio Sanfilippo}\\
Computer Laboratory, University of Cambridge\\
New Museums Site, Pembroke Street, Cambridge, CB2 3QG, UK\\
{\tt Ann.Copestake@cl.cam.ac.uk, bemj1@phx.cam.ac.uk, 
Antonio.Sanfilippo@cl.cam.ac.uk}\\[0.1in] 
{\large Horacio Rodr\'{\i}guez}\\
Dept. Llenguatges i Sistemes Informatics, 
Universitat Polit\`{e}cnica de Cataluyna\\
PAU GARGALLO 5, 08028 Barcelona, Spain\\[0.1in]
{\large Piek Vossen}\\
Department of English, University of Amsterdam \\
Spuistraat 210, 1012 VT Amsterdam, The Netherlands\\[0.1in]
{\large Simonetta Montemagni, Elisabetta Marinai}\\
Instituto di Linguistica Computazionale
Via della Faggiola 32, I-56100, Pisa, Italy
\end{center}

\section*{Abstract}
We describe an approach to 
the representation of lexical translation equivalence 
which allows cross-linguistic 
generalisations to be expressed 
within a typed unification-based formalism, 
and illustrate how a large scale multilingual 
lexical knowledge base that uses this representation
may be constructed semi-automatically from machine readable dictionaries.  \\
{\bf Topic areas: the lexicon, machine translation.}

\section{Introduction}

This paper describes the multilingual aspects of the
lexical knowledge base (LKB) system which has been designed 
and implemented as part of the 
ACQUILEX project\footnote{`The Acquisition of lexical knowledge
for Natural Language Processing systems' (ESPRIT BRA-3030); see Briscoe(1991).}
to allow the representation of 
syntactic and semantic information extracted from 
machine readable dictionaries (MRDs).
The LKB's representation language (LRL) is based on
typed feature structures; 
Copestake (1992) and Sanfilippo and Poznanski (1992)
describe the LKB and the LRL and illustrate
how large scale monolingual lexicons 
have been constructed semi-automatically in the LKB for subsets of the
four languages involved (English, Spanish, Dutch and Italian), using a
common type system.
In this paper we describe our use of
the LRL to represent multilingual information in the form of links 
between translation equivalent lexical entries, 
and discuss the techniques we have implemented to allow large numbers of
such links to be generated (semi-)automatically from MRDs.

Recently, there has been considerable interest in encoding
multilingual transfer rules in unification-based formalisms, aiming
for declarativeness and bidirectionality, but allowing sufficient
expressiveness to deal with lexical `gaps'; specialisation, and so
forth (e.g. Zajac, 1989; 
Estival {\it et al.}, 1990; Alshawi {\it et al.}, 1991).  
Our concept of
translation equivalence maintains these advantages, but abstracts away from
MT transfer rules; it is intended to allow the expression of
linguistic and lexicographic generalisations in constructing
a multilingual lexicon which would support a variety of approaches to MT
and could also be used by
human translators, language teachers, lexicographers and linguists,
given suitable interfaces.
MT transfer rules do not necessarily
explicitly link the monolingual lexical entries; however
this is essential for the multilingual LKB to be useful
more generally.  

The mechanism for 
expressing equivalence between feature structures
described here 
is quite general and abstract but our use of it
has similarities with the
`Shake-and-Bake' approach to MT (Whitelock, 1991; Beaven, 1992) 
because of its strongly lexicalist orientation and its reliance 
on unification of semantic indices.
However, in addition, 
our formalism makes it possible to state
generalisations about translation equivalence 
between classes of lexical entries 
and
cross linguistic relationships between lexical processes.
Our long term goal is 
to develop a linguistically-motivated theory of the multilingual lexicon,
capturing the appropriate generalisations and lexical rules.
Sanfilippo {\it et al.} (1992) explore an account of MT transfer similar to
that of Beaven and Whitelock using a multilingual lexicon constructed
in the framework presented below.

We define lexical
translation equivalence in terms of cross-linguistic links, {\em tlinks},
between the lexical entries in the monolingual
lexicons. 
Although in the simplest and commonest cases
unmodified pairs of lexical entries
can be treated as
translation equivalents, in general we have to allow for
`mismatches' such as 
differences in argument ordering, plurality, and specificity of
reference, 
and for `lexical gaps', 
where a word sense in one language has to be translated by a
phrase in the other. 
We use inheritance from both lexical entries and 
rules in tlinks; this makes them compact 
while ensuring that the multilingual and monolingual
components are compatible.  
The tlink mechanism does not
involve any extensions to the LRL.

In the next section we briefly introduce the salient features of the 
LRL.
We then describe the tlink mechanism and discuss some examples of
the representation of translation equivalence between nouns.
In section~4 we illustrate how we can generate
at least the simple
links between the monolingual entries (semi-)automatically by
making use of bilingual MRDs and conclude by briefly discussing the 
use of the LKB in MT.

\section{The Lexical Representation Language}

The LRL is fully described in papers in 
Briscoe et al (1992, forthcoming) and various aspects
are discussed in Briscoe (1991),
Copestake and Briscoe (1991),
Copestake (1992) and Sanfilippo and Poznanski (1992).  Here we therefore 
only give a brief informal description of the relevant aspects.

The LRL is based on
typed feature structures similar to those described by Carpenter
(1990, 1991).
However we adopt a slightly  different notion of well-formedness.  
In our system every type must have exactly one associated
feature structure (FS) which acts as a {\it constraint} on all FSs
of that type; all well-formed FSs of that
type must be subsumed by the constraint.  
The constraint also defines which features are {\em
appropriate} for a particular type; a well formed FS
may only contain appropriate features.  
Constraints are inherited by
all subtypes of a type, but a subtype may introduce new features
(which will be inherited as appropriate features by all its subtypes).
A constraint on a type is a well-formed FS of that
type; all constraints must therefore be mutually consistent.

An example of a monolingual lexical entry, derived automatically from
LDOCE (Longman Dictionary of Contemporary English, Procter (1978))
entry for chocolate (in the drinking chocolate sense) 
is shown below (Figure~1).
\begin{verbatim}
chocolate_L_1_4
<sense-id : dictionary> = "LDOCE"
<sense-id : homonym-no> = "1"
<sense-id : sense-no> = "4"
<sense-id : ldb-entry-no> = "5902"
<cat : m-feats : agr : pers> = 3
<cat : m-feats : count> = BOOLEAN
<cat : m-feats : agr : num> = SG
<lex-noun-sign rqs> < DRINK_L_2_1 <lex-noun-sign rqs> .
\end{verbatim}
\vspace{100mm}
\nopagebreak
\begin{center}
Figure 1
\end{center} 
(Bold font is used for types, small capitals for features, 
the boxes indicate parts of the FS which are not shown completely.
In the path notation \verb+<+ is used for default inheritance from a
named FS.)
The type system and default inheritance mechanism 
allows a relatively
compact lexical entry to be
expanded into a detailed FS.
The syntactic and formal semantic portions of
the lexical sign are relatively conventional, however we also encode
detailed lexical semantic information, the {\sc rqs} (``relativised 
qualia structure'', Calzolari 1991).  
All the monolingual lexicons in the LKB make use of the same type system.

We also encode grammar and lexical rules as typed feature structures,
which represent relationships between two or more signs
(i.e. FS descriptions of single words or phrases).
A lexical rule is a FS of type {\bf lexical-rule} which
is a subtype of {\bf rule} ({\bf top} is the most general type).  
\begin{verbatim}
rule (top)
< id > = rule-id
< 0 > = sign
< 1 > = sign.

lexical_rule (rule)
< 0 > = lex_sign
< 1 > = lex_sign.
\end{verbatim}
The expanded constraints for these types are:
\begin{center}
{\scriptsize
   $\avmplus{\att{rule}\\
             \attvaltyp{rule-id}{rule-id}\\
             \attvaltyp{0}{sign}\\
             \attvaltyp{1}{sign}}$} 
\hspace{1in}
{\scriptsize
   $\avmplus{\att{lexical\_rule}\\
             \attvaltyp{rule-id}{rule-id}\\
             \attvaltyp{0}{lex\_sign}\\
             \attvaltyp{1}{lex\_sign}}$} 
\end{center}
Thus all lexical rules have to have the features 0 
and 1 which must
both have values which are of type {\bf lex\_sign}.  
Lexical rules can be regarded as a means of generating 
new lexical signs; if a lexical entry can be unified 
with the FS at the end of the path 
\verb|<1>| in the lexical rule,
then the FS at the end of
the path \verb|<0>| is a new lexical sign.  
Alternatively they can be regarded statically,
as expressing the relationship between two existing lexical signs.
We use lexical rules both to represent morphological derivation and 
sense extension (Copestake and Briscoe 1991, Briscoe and Copestake 1991).

\section{Representation of translation equivalence}

We represent cross-linguistic relationships between word senses as tlinks.
In general there may be a many-to-many equivalence between word senses,
but each possibility is represented by a single tlink. 
In the common, simple cases, word senses may be represented as being directly
translation equivalent, and it is unnecessary to augment the
monolingual information, other than simply to assert that a link is
present (see {\bf simple-tlink}, below).
The tlink mechanism, however, allows the
monolingual information to be augmented with translation specific
information, in a variety of ways.  

A tlink is simply a FS of type {\bf tlink}, 
which is to be read as stating that two
FSs (the ``output structures'') are to be regarded as
translation equivalents.  
The tlink encodes the relationship between
the input word senses and these output structures;  
it can be viewed as describing how
lexical entries may be transformed
into translation equivalent pairs.
A complete tlink is essentially a relationship between two {\bf rule}s
(as defined above)
where the rule inputs
have been instantiated by the representations of the word senses 
in the source and target languages which are to
be linked and where the rule outputs are translation equivalent.
The type {\bf tlink} is defined as follows:
\begin{verbatim}
tlink (top)
< tlink-id > = tlink-id
< sfs > = rule
< tfs > = rule
< sfs : 0 : sem : ind > = < tfs : 0 : sem : ind >.
\end{verbatim}
The third line indicates
equivalence of the variables in the two output structures in the
particular monolingual encoding of semantic information that we are
currently adopting.
For all tlinks the FSs at the end of the paths 
\verb+< sfs : 0 >+ and \verb+< tfs : 0 >+ will be translation equivalent.
For all tlinks at least the paths \verb+< sfs : 1 >+ and \verb+< tfs : 1 >+ 
have to be instantiated by lexical entries to produce the 
complete tlink\footnote{Tlinks and lexical rules are both symmetrical 
and reversible; we use 
the terminology source (sfs), target (tfs), 
input (1) and output (0) solely for ease of exposition.}

By defining types of tlinks the concept of translation equivalence can be
constrained and generalisations can be encoded.
The commonest and simplest cases of translation equivalence
can be represented as {\bf simple-tlink}s.
\begin{verbatim}
simple-tlink (tlink)
< sfs : 0 > = < sfs : 1 >
< tfs : 0 > = < tfs : 1 >.
\end{verbatim}
A {\bf simple-tlink} is applicable in the case where two lexical entries 
which denote single place predicates (nouns etc) are
straightforwardly translation equivalent, without any transformation
being necessary.
(For verbs more argument equivalence specifications are necessary, see
Sanfilippo {\it et al.} (1992).)

Like lexical rules tlinks can be regarded statically or dynamically
as generating new FSs;
given a FS in one language, and an appropriate tlink,
unification with
the FS at the end of the appropriate path 
(eg \verb+< sfs : 0 >+ ) in
the tlink, will result in the FS at the end of the other
output path being returned (eg \verb+< tfs : 0 >+ ).

Assuming that the LDOCE sense {\it chocolate} 1 4, is translation
equivalent to the Van Dale {\it chocolade} 0 2, we would have 
the tlink:
\begin{verbatim}
simple-tlink
< sfs : 1 > << chocolate_L_1_4
< tfs : 1 > << chocolade_V_0_2.
\end{verbatim}
where \verb+<<+, indicates non-default
inheritance from a named FS (the particular lexical entries).
The ``syntactically sugared'' version, which appears in files,
is:
\begin{verbatim}
chocolate_L_1_4 / chocolade_V_0_2
simple-tlink .
\end{verbatim} 

Some restrictions on translation can be expressed by
making the target or source
FSs more specific.
For example both {\it maestro} and {\it maestra} in Spanish can be
translated as {\it teacher} in English; the restriction that {\it maestro}
denotes a male teacher and {\it maestra} a female one, 
can be encoded as follows:
\begin{verbatim}
teacher_1 / maestro_1 :
simple-tlink
< sfs : 0 : rqs : sex > = male.

teacher_1 / maestra_1 :
simple-tlink
< sfs : 0 : rqs : sex > = female. 
\end{verbatim}
Alternatively we can define a type {\bf human-tlink} and 
state as a constraint that the values for the {\sc sex} feature must
be equivalent in the translation equivalent feature structures.
\begin{verbatim}
human-tlink (simple-tlink)
< sfs : 0 : rqs : sex > = < tfs : 0 : rqs : sex >.
\end{verbatim}
The restrictions would then follow, assuming that the Spanish lexical
entries were appropriately instantiated,
and would apply to the whole class.

Somewhat rarer and more complex cases of linking arise 
when the changes to the FS are
those such as pluralisation, which is a process 
that has to be represented separately, and which has to be viewed as a
transformation of a FS rather then a simple restriction.
For example, a lexical/morphological rule for plural formation, 
which would be formulated
anyway for use in parsing/generation, can be used in a tlink: 
we encode the idea that the equivalence is to be defined between a
basic lexical entry and a lexical entry after rule application,
by instantiating one half of the tlink with the appropriate lexical rule.

For example {\it furniture} can be encoded as translation equivalent to
the plural {\it muebles} by
specifying that the named rule `plural' has to be
applied to the base sense in Spanish.
\begin{verbatim}
furniture_1 / mueble_1 :
tlink
< sfs : 0 > = < sfs : 1 >
< tfs > << plural <> .
\end{verbatim}
This tlink can be represented figuratively as shown in 
Figure~2; unlabelled arrows indicate token identity between 
FSs.
\begin{center}
\begin{picture}(105,25)(-5, 15)
\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(50,35){\makebox(0,0){\bf 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){muebles}}
              \put(77,30){\makebox(0,0){plural}}
              \put(90,25){\vector(-1,0){25}}
              \put(95,25){\makebox(0,0){TFS1}}
              \put(95,18){\makebox(0,0){mueble}}
\end{picture}\\[.1in]
Figure~2
\end{center}
Since the singular form of {\it mueble} would not unify with the feature
structure at the end of the output path \verb+< tfs : 0 >+
a translation of ``mueble'' as ``furniture'' would not be generated by
this tlink.  
The translation of ``mueble'' as
``a piece of furniture'', for example, would be encoded as a distinct tlink.
 
In general when a word has to be translated by means of a
phrase, 
a representation for that phrase can be built up in the tlink,
to make an
entire phrase translation equivalent to a word sense.
This mechanism is used in the case of `lexical gaps'. For example 
Spanish {\it novillo} can be translated as {\it young
bull}; the translation can be represented as follows
(where `forward-application' is a grammar-rule through which
adjectives and nouns are combined):
\begin{verbatim}
young_1 bull_1 / novillo_1 :
tlink
< sfs > << forward-application <>.
\end{verbatim}
(The use of a specific phrase in a tlink 
does not commit an MT system to generate exactly that phrase; the
meaning representation could be used by a system that generated from
logical form, for example.)

In some cases the existence of a tlink between two lexical items
implies a further translation relationship.  For example, in English,
there is a regular sense extension such that a word used primarily to
denote an animal can also be used to denote the meat of that animal
(e.g. {\it lamb}, {\it chicken}, see Copestake and Briscoe (1991)). 
A similar sense extension rule
applies to Italian (\"{O}stling 1991)
but in Dutch a compound is generally
used ({\it lam}, {\it lamvlees}).
To represent the relationship between these lexical rules 
we define the type {\bf tlink-rule}:
\begin{verbatim}
tlink-rule (top)
< id > = tlink-rule-id
< t0 > = tlink
< t1 > = tlink
< srule > = lexical-rule
<srule : 1 > = < t0 : sfs : 1 >
<srule : 0 > = < t1 : sfs : 1 >
< trule > = lexical-rule
<trule : 1 > = < t0 : tfs : 1 >
<trule : 0 > = < t1 : tfs : 1 >
\end{verbatim} 
By stating that the lexical rule for animal-grinding is linked with
that for compounding with {\it vlees},
we can for example automatically generate the
relationship between {\tt lamb\_2} and {\tt lamvlees} from
a simple tlink between {\tt lamb\_1} and {\tt lam},
see Figure~3.
\footnote{Since we are just making use of the monolingual sense extension
mechanism here we can rely on that to handle
cases where the sense extension is blocked (e.g. {\it pig}).}
\begin{center}
\begin{picture}(140,80)(-20,-40)
\thinlines    \put(5,25){\makebox(0,0){SFS1}}
              \put(5,18){\makebox(0,0){lamb1}}
              \put(10,25){\vector(1,0){25}}
              \put(40,25){\makebox(0,0){SFS0}}
              \put(40,18){\makebox(0,0){lamb1}}
              \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){lam}}
              \put(90,25){\vector(-1,0){25}}
              \put(95,25){\makebox(0,0){TFS1}}
              \put(95,18){\makebox(0,0){lam}}

              \put(5,15){\vector(0,-1){35}}
              \put(-10,0){\makebox(0,0){\shortstack{animal-\\ grinding}}}
              \put(95,15){\vector(0,-1){35}}
              \put(110,0){\makebox(0,0){\shortstack{vlees\\ compounding}}}

              \put(5,-25){\makebox(0,0){SFS1}}
              \put(5,-32){\makebox(0,0){lamb2}}
              \put(10,-25){\vector(1,0){25}}
              \put(40,-25){\makebox(0,0){SFS0}}
              \put(40,-32){\makebox(0,0){lamb2}}
              \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){lamvlees}}
              \put(90,-25){\vector(-1,0){25}}
              \put(95,-25){\makebox(0,0){TFS1}}
              \put(95,-32){\makebox(0,0){lamvlees}}
\end{picture}\\[.1in]
Figure~3
\end{center}
There are many examples of such correspondences; for example 
the English sense extension between
trees and their fruits ({\it pear}\/ etc)
is mirrored in Italian with a gender distinction; the trees are
masculine but the fruits feminine ({\it pero}, {\it pera})\footnote{It does
not necessarily matter for translation purposes
whether the rule can fully predict the effects of the sense extension;
even if the rule is used statically to encode the
regular aspects of the relationship between two lexicalised items, 
an appropriate translation link will be generated
if the monolingual processes are sufficiently similar.}.

Our earlier examples can in fact be described more generally.
A tlink-rule can be defined for human-denoting nouns so that given the 
equivalence between two entries of one gender 
the tlink for the other gender can be produced.
Since gender in English is normally unmarked, one instantiation of
this is with a `null lexical rule', which states equality between
two FSs.
Thus given the tlink between
{\it teacher} and {\it maestra} we can automatically generate that
between {\it teacher} and {\it maestro}, see Figure~4.
\begin{center}
\begin{picture}(140,80)(-20,-40)
\thinlines    \put(5,25){\makebox(0,0){SFS1}}
              \put(5,18){\makebox(0,0){teacher}}
              \put(10,25){\vector(1,0){25}}
              \put(40,25){\makebox(0,0){SFS0}}
              \put(40,18){\makebox(0,0){\shortstack{teacher\\ +male}}}
              \put(50,35){\makebox(0,0){\bf human-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){maestro}}
              \put(90,25){\vector(-1,0){25}}
              \put(95,25){\makebox(0,0){TFS1}}
              \put(95,18){\makebox(0,0){maestro}}

              \put(5,15){\vector(0,-1){35}}
              \put(-10,0){\makebox(0,0){equality}}
              \put(95,15){\vector(0,-1){35}}
              \put(110,0){\makebox(0,0){masc/fem}}

              \put(5,-25){\makebox(0,0){SFS1}}
              \put(5,-32){\makebox(0,0){teacher}}
              \put(10,-25){\vector(1,0){25}}
              \put(40,-25){\makebox(0,0){SFS0}}
              \put(40,-32){\makebox(0,0){\shortstack{teacher\\ +female}}}
              \put(50,-15){\makebox(0,0){\bf human-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){maestra}}
              \put(90,-25){\vector(-1,0){25}}
              \put(95,-25){\makebox(0,0){TFS1}}
              \put(95,-32){\makebox(0,0){maestra}}
\end{picture}\\[0.1in]
Figure~4
\end{center}

The count-mass
discrepancy which is responsible for the need to invoke pluralisation
in the furniture-muebles example can also be generalised. 
Rather than
manually specifying the translations of both the singular and plural
count nouns for every case
we can specify that the
translation equivalent of the singular noun will be some more complex
construction which individuates the mass noun, equivalent to the use of a
phrase such as ``piece of'', see Figure~5.
\begin{center}
\begin{picture}(140,80)(-20,-40)
\thinlines    \put(5,25){\makebox(0,0){SFS1}}
              \put(5,18){\makebox(0,0){furniture}}
              \put(10,25){\vector(1,0){25}}
              \put(40,25){\makebox(0,0){SFS0}}
              \put(40,18){\makebox(0,0){furniture}}
              \put(50,35){\makebox(0,0){\bf 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){muebles}}
              \put(77,30){\makebox(0,0){plural}}
              \put(90,25){\vector(-1,0){25}}
              \put(95,25){\makebox(0,0){TFS1}}
              \put(95,18){\makebox(0,0){mueble}}

              \put(5,15){\vector(0,-1){35}}
              \put(-10,0){\makebox(0,0){equality}}
              \put(95,15){\vector(0,-1){35}}
              \put(110,0){\makebox(0,0){equality}}

              \put(5,-25){\makebox(0,0){SFS1}}
              \put(5,-32){\makebox(0,0){furniture}}
              \put(22,-20){\makebox(0,0){individuation}}
              \put(10,-25){\vector(1,0){25}}
              \put(40,-25){\makebox(0,0){SFS0}}
              \put(40,-32){\makebox(0,0){\shortstack{piece of\\ furniture}}}
              \put(50,-15){\makebox(0,0){\bf 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){mueble}}
              \put(90,-25){\vector(-1,0){25}}
              \put(95,-25){\makebox(0,0){TFS1}}
              \put(95,-32){\makebox(0,0){mueble}}
\end{picture}\\[0.1in]
Figure 5
\end{center}

\section{Finding translation equivalents}

The basic difficulty
with automatic generation of tlinks (or any
representation of lexical translation equivalence) from MRDs
is selection of the correct sense.  We are attempting to link
entries derived from monolingual dictionaries.  Bilingual
dictionaries, where they do discriminate between senses, 
may use a scheme which differs from any monolingual, 
and the translations given in bilinguals typically
have no sense marking.
Consider the following, taken from the English---Spanish VOX dictionary
(Biblograf 1988):
\begin{quote}
{\sf
{\bf crush} {\it s}. compresi\'on, presi\'on, aplastiamiento, machacadura,
estrujamiento, \\
\hspace{3cm}estruj\'on. {\it 2} cantidad de material machacado, estrujado, 
etc$\ldots$ }
\end{quote}
Not all of these translations will be appropriate for all the senses
given in LDOCE for {\it crush}, and even those which are appropriate may
also have inappropriate senses.

We thus use bilingual dictionaries to give a range of possible
LKB entries in a target language, 
when given an LKB entry in the source language.
Currently this is done using 
orthographic information alone and we can only generate simple tlinks 
automatically.  
This candidate list of translations 
is input to a matching program, {\sc lucifer},
which returns the sense or senses most semantically
similar to the source sense.

\subsection{Lucifer}

{\sc lucifer} is a general purpose utility for comparison of
typed FSs (see Copestake and Jones (1991) for a fuller description).
A statistic is assigned to each candidate FS, with the magnitude
of the statistic proportional to the quality of match.
This is compiled
by descending both FSs
simultaneously, comparing structure along all the branches.
If in the course of the FS descent any
mismatches are found, these will decrease the statistic for the match. 
On reaching the terminal nodes, a numeric value is returned dependent on
the similarity of the values at equivalent leaf nodes on the two
structures. 
The values are defined as follows:
\begin{tabbing}
\hspace{4cm}\=Exact match \hspace{4cm} \=+1.0 \\
\>Source subsumes target \>+0.5 \\
\>Target subsumes source \>+0.5 \\
\>Type mismatch \>-Total \\
\end{tabbing}
Thus subsumption is
taken to indicate a similarity between the FSs,
but less so than an exact match between the types..  
A type incompatability at any node 
gives a negative value of the
magnitude of the statistic of the whole FS, were it to
match perfectly. This ensures that FSs containing a
serious mismatch will always have a small statistic compared with
those that do not contain such a mismatch.

The problem with assigning a simple statistic
is that no distinction is made in the importance of the
components of a FS. 
Orthography, for example, should be ignored 
in cross-linguistic comparison of lexical entries.
Thus 
each branch of the matched FSs has a unique weight, 
ranging between 0.0 and 1.0, which modifies the values returned.
Weights are multiplied by the nodal value of their
descendant node to give a set of inputs to the parent node. These are
summed to form the nodal value of the non-terminal parent node. The
process is then repeated in successive ancestral levels until the root
node of the FS is assigned a nodal value. This value is
used as the statistic qualifying the whole match.

Weights cannot be assigned by hand, as 
we are dealing with large numbers of entries and comparison
of different classes of words may involve different specific features
being highlighted as important. 
Hence a learning procedure is used to modify the
weights for the specific matching task being carried
out and this involves training,
either by human confirmation of {\sc lucifer}'s initial choices
or by using examples of unique
translations from the bilingual.  

All weights are initialised with a value of 0.5 and
updated on the basis of the correct matching FS(s). 
If
there are any mismatches in the structure, the weight of the branches
leading to this mismatch is decreased - if there is a match the weight
is increased.  As the mismatch is far more serious than the match the
weight is decreased by far more than than it is increased in the case
of a match. 
The formula used is a variation on the error
signal for the logistic activation function in classic feed-forward
back-propagation networks using the generalised delta rule (Rumelhart
et al. 1986).
If the weight on a branch is $\omega_{i}$ 
\[ \partial\omega_{\tau} =
\eta\frac{\alpha_{\tau}}{\:Total\:}\omega_{\tau}(1 - \omega_{\tau})\] 
where
$\alpha_{i}$ is the activation of the descendant node 
and
$\eta$ is the learning rate.

\subsection{Generating tlinks}

We have implemented a system which combines {\sc lucifer} with an
interface to the bilingual dictionaries.  After the initial training
phase matching will only involve human intervention when {\sc lucifer}
cannot find clear winners because no FS has a 
sufficiently high score. 
The full paper will report the results of several large scale
experiments to generate simple tlinks automatically for the subsets of
the vocabulary for which we have constructed monolingual
lexicons.

In many cases the various candidates given by 
the bilinguals are distinguishable on the basis of their lexical semantic 
type alone since we
have adopted a relatively fine-grained classification; thus the full power 
of {\sc lucifer}'s matching algorithm is not needed.  For example the senses
of {\it crush} given in LDOCE divide into the
incompatible lexical
semantic types, {\bf dur\_event} (non-point like event),
{\bf c\_subst\_nat} (comestible natural substance) and {\bf abstract}
(we have not yet attempted any subclassification of abstract,
non-event denoting, nouns).

However there are cases where more
detailed comparison of the FSs is necessary;
{\it chocolate} in English can refer to the solid substance or to
drinking chocolate and both these senses share the same rqs type 
{\bf c\_art\_subst},
whilst {\it cioccolato} in Italian is specific to the solid substance,
{\it cioccolata} is used for the substance or the drink, and 
{\it cioccolatino} for the individual sweet.
From the bilingual we get possible links from chocolate\_L\_1\_4
to all the senses of these three words.
{\it cioccolatino} has rqs type {\bf c\_art\_obj} which is
incompatible with that of chocolate\_L\_1\_4 and it is therefore 
given a very low matching statistic.
{\sc lucifer} discriminates between the other possibilities on the
basis of the different value for {\sc physical-state} and for the
telic role.  Thus the translation link will be made between 
chocolate\_L\_1\_4 and cioccolata\_G\_0\_2 (see Figure~1 and Figure~6). 

\section{Machine translation}

If the output forms in the tlink alone are considered they are
essentially equivalent to transfer rules between FSs.
Existing MT systems could make use of the tlinks by 
transforming these parts of the tlink into transfer rules in the
format required.  (The issues of defining correspondences between
features used in the LKB and those of other systems are essentially
the same as in the monolingual case, and so are not considered further
here.)
For example the ELU system (Estival et al 1990), uses 
lexical transfer rules 
which could be derived from tlinks on the
assumption that transfer variables are equivalent to reentrancy
between tlink output FSs.
The BCI system (Alshawi et al 1991) uses transfer between quasi logical
forms; the appropriate lexical transfer rules between predicates
can be derived from tlinks under the same assumptions. 
Since at its most general 
the tlink mechanism states correspondences between FSs 
its use is not specific to transfer based MT.  
In our current LKB the predicates in the logical forms are not only 
language specific but 
refer to senses in particular MRDs.  Eventually we would hope to avoid this,
but a `deep' transfer/interlingua approach,
based on Wilks(1991) suggestion that LDOCE senses be used in an interlingua,
might still be able to utilise an LKB built using our current techniques.

As mentioned in the introduction Whitelock and Beaven's 
`Shake-and-Bake' approach is the most natural way of utilising the LKB 
in MT.  The LRL allows improvements on their approach 
in that generalisations can be expressed using 
the type system.
Sanfilippo {\it et al.} (1992) explore this in more detail, 
and describe approaches to translation mismatches involving 
language-specific lexicalisation patterns for verbs
using the mechanisms presented here. 
However we regard MT as only one possible application; ultimately the
multilingual LKB should be able to support the needs of linguists,
lexicographers and others who need access to substantial quantities of
cross-linguistic information and who
wish to express cross-linguistic generalisations about the lexicon.

\section*{References}
\newcommand{\book}[4]{\item #1 (#4) {\it #2,} #3.}
\newcommand{\booknoauthor}[3]{\item #1 (#3) {\it #2,}}
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\newcommand{\unpub}[4]{\item #1 (#4) {\it #2,} #3.}
\newcommand{\bookart}[7]{\item #1 (#7) `#2' in #5 (eds.), {\it #4,} #6, pp.~#3.}
\newcommand{\bookartnopp}[6]{\item #1 (#6) `#2' in #4 (eds.), {\it #3,} #5.}
\newcommand{\forthart}[6]{\item #1 (#6, forthcoming) `#2' in #4 (eds.), {\it #3,} #5.}
\newcommand{\journart}[6]{\item #1 (#6) `#2', {\it #3,} {\it vol.#4,} #5.}
\newcommand{\procart}[6]{\item #1 (#6) `#2', {\it Proceedings of the
#3,} #4, pp.~#5.}
\newcommand{\forthprocart}[5]{\item #1 (#5) `#2', 
to appear in {\it Proceedings of the #3,} #4}
\newcommand{\procartnopp}[5]{\item #1 (#5) `#2', {\it Proceedings of the
#3,} #4.}

\begin{list}{}
   {\leftmargin 1.6em
    \itemindent -\leftmargin
    \itemsep 0pt plus 1pt
    \parsep 0pt plus 1pt}
\procart{Alshawi, H., D. Carter, M. Rayner and B. Gamb\"ack}
    {Translation by Quasi Logical Form Transfer}
    {29th ACL}
    {Berkeley, California}
    {161--168}
    {1991}
\unpub{Beaven, J.~L.}
    {Lexicalist Unification-Based Machine Translation}
    {PhD thesis, University of Edinburgh}
    {1992}
\booknoauthor{Biblograf (publisher)}
    {VOX Diccionario Ingl\'{e}s-Espa\~{n}ol Espa\~{n}ol-Ingl\'{e}s}
    {1988}
\bookart{Briscoe, T.}
    {Lexical Issues in Natural language Processing}
    {39--68}
    {Natural Language and Speech}
    {E. Klein and F. Veltman}
    {Springer-Verlag}
    {1991}
\bookart{Briscoe, T. and A. Copestake}
         {Sense extensions as lexical rules}
         {12--20}
         {Computational approaches to non-literal language: metaphor,
metonymy, idiom, speech acts, implicature}
         {D.~Fass, E.~Hinkelman and J.~Martin}
         {Proc. of IJCAI Workshop, Sydney}
         {1991}
\forthedbook{Briscoe, T., A.~Copestake and V.~de Paiva} 
{Default Inheritance in Unification-Based Approaches to the Lexicon}
     {CUP}
     {1992}
\procart{Calzolari, N.}
     {Acquiring and representing semantic information in a
      lexical knowledge base}
     {ACL SIGLEX Workshop on Lexical Semantics and Knowledge
Representation}
     {Berkeley, California}
     {188--197}
     {1991}
\procart{Carpenter, B.}
    {Typed feature structures: Inheritance, (In)equality and Extensionality}
    {First International Workshop on 
Inheritance in Natural Language Processing}
    {Tilburg, The Netherlands} 
    {9--18}
    {1990}
\book{Carpenter, R.}
    {The Logic of Typed Feature Structures}
    {Cambridge University Press, Tracts in Theoretical Computer Science}
    {1991, in press}
\forthprocart{Copestake, A.}
{The ACQUILEX LKB: representation issues in semi-automatic
acquisition of large lexicons}
{ANLP-92}
{Trento, Italy}
{1992} 
\procart{Copestake, A. and T.~Briscoe}
     {Lexical Operations in a Unification Based Framework}
     {ACL SIGLEX Workshop on Lexical Semantics and Knowledge
Representation}
     {Berkeley, California}
     {88-101}
     {1991}
\unpub{Copestake, A. and B.~Jones}
     {Support for multi-lingual lexicons in the LKB system}
     {ms University of Cambridge, Computer Laboratory}
     {1991}
\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}
    {Austin,Texas}
    {131--143}
    {1990}
\unpub{\"{O}stling, A.}
    {Sense extensions in the Italian food subset} 
    {ACQUILEX working paper}
    {1991}
\book{Procter, P. (ed)}
    {Longman Dictionary of Contemporary English}
    {Longman, London}
    {1978}
\bookart{Rumelhart, D.~E., G.~E. Hinton and R.~J. Williams}
    {Learning Internal Representations by Error Propagation}
    {318--362}
    {Parallel Distributed Processing --- Explorations in the
microstructure of Cognition Volume 1}
    {D.~E. Rumelhart and J.~L. McClelland}
    {Cambridge, Massachusetts}
    {1986}
\unpub{Sanfilippo, A., T.~Briscoe, A.~Copestake, T.~Marti and A.~Alonge} 
    {Translation equivalence and lexicalisation} 
    {ms University of Cambridge, Computer Laboratory}
    {1992}
\forthprocart{Sanfilippo, A. and V. Poznanski}
{The Acquisition of Lexical Knowledge from Combined Machine-Readable
Dictionary Sources}
{ANLP-92}
{Trento, Italy}
{1992}
\unpub{Whitelock, P.}
     {Shake-and-bake translation}
     {submitted to Coling-92}
     {1991}
\unpub{Wilks, Y.}
    {More introduction and a bibliography}
    {Machine Translation: ACL tutorial notes}
    {1991}
\procart{Zajac, R.}
    {A transfer model using a typed feature structure rewriting system
with inheritance}
    {27th ACL}
    {Vancouver, BC}
    {1--7}
    {1989}

\end{list}
\end{document}










