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\begin{center}
{\Large \bf Lexical Issues in Natural Language Processing}\\
\smallskip
Ted Briscoe\\
\smallskip
University of Cambridge Computer Laboratory\\
New Museums Site, Pembroke Street, Cambridge, CB2 3QG, UK\\
\smallskip
November 1991\\
\smallskip
(in E. Klein and F. Veltman (eds.),
{\it Natural Language and Speech,}
Springer-Verlag, 1991)\\
\bigskip
ESPRIT BRA-3030 ACQUILEX Working Paper No 041
\end{center}
\newpage
\setcounter{page}{1}
\begin{center}
{\LARGE Lexical Issues in Natural Language Processing} \\
\ \\
Ted Briscoe\\
Computer Laboratory, University of Cambridge \\
Pembroke Street, Cambridge, CB2 3QG, UK\\
ejb@cl.cam.ac.uk
\end{center}


\section{Introduction}
In this paper, I will briefly describe the role of the lexicon in
natural language processing (NLP) applications and will go on to
discuss a number of issues in lexical research and in the design and
construction of lexicons for practical NLP applications. I will survey
relevant research in Europe, America and Japan; however, in a paper of
this length it is not possible to consider every instance of a
particular approach, so neither the text nor references should be
taken to be exhaustive.

In recent years, the lexicon has become the focus of considerable research  
in (computational) linguistic theory and NLP; the reasons for this trend are
both theoretical and practical. Within linguistics, the role of the
lexicon has increased as more and more linguistic generalisations have
been seen to have a lexical dimension. Within NLP, the lexicon has
increasingly become the chief `bottleneck' in the production of
habitable NLP systems offering an adequate vocabulary for the intended
application. This has led to the use of machine-readable versions of
conventional dictionaries in an attempt to develop substantial
lexicons for NLP in a resource efficient fashion\footnote{I shall use
the term `dictionary' to refer to the conventional printed object for
human use and `lexicon' for a formal and possibly implemented
dictionary intended either as a component of linguistic theory or of an
NLP system.}. At the same time, dictionary publishers and
lexicographers have realised the potential benefits of information
technology in the dictionary production process and also the potential
new markets for their products that might be created by commercial
development of language technologies.  This latter possibility creates
the very exciting opportunity for collaborative research and
development between these apparently rather disparate communities
which, I believe, would substantially remove the existing bottleneck
for NLP systems and would provide a major impetus to theoretical
research on the lexicon.

In the next section, I review relevant research in recent
linguistic theory and describe the highly-structured,
hierarchical and `generative' conception of the lexicon which is
emerging from this work. I then go on to discuss the advantages and
disadvantages of exploiting machine-readable dictionaries and progress 
which has been made, to
date, on constructing substantial lexicons with such resources. I also
discuss several new projects which, in reaction to this trend, have
opted for manual construction. In the fourth section, I provide more
detailed examples illustrating some of these trends and developments,
drawn from the ACQUILEX project, through which I and my colleagues
have been both exploring the utility of machine-readable dictionaries for 
NLP and contributing
to the development of a theoretically-motivated, but substantial and
computationally-tractable, multilingual lexicon. In the conclusion, I
discuss the more active role that lexicographers and dictionary
publishers have begun to play in lexical research and suggest how this
development might be harnessed to facilitate solutions to outstanding
problems in lexicon design and development.

\section{The Lexicon in Linguistic Theory}

At least since Bloomfield (1933), the lexicon has usually been viewed
as the repository of idiosyncratic and unpredictable facts about
lexical items organised as an (alphabetic) list; for example, that {\it
kill} in English means `x cause y to die' and is a transitive
verb with regular morphology. On the other hand, the fact that the
subject of {\it kill} appears before it in typical English sentences
or that its past participle is {\it killed} were taken to be
predictable and quite general statements about English syntax and
morphophonology which should be stated independently of the lexicon.
However, within generative linguistic theory since the 1970s there has
been a consistent increase in the role of the lexicon in capturing
linguistic generalisations, both in the sense that more and more of
the rules of grammar are coming to be seen as formal devices which
manipulate (aspects of) lexical entries and in the sense that many of
these rules are lexically-governed and must, therefore, be restricted
to more finely specified classes of lexical items than can be obtained
from traditional part-of-speech classifications. As the importance of
the lexicon has increased, so the role of other components of the
overall theory of grammar has declined; thus in some contemporary
theories, the syntactic component is reduced to one or two general
principles for the combination of constituents, whilst all the
information concerning the categorial identity and mode of combination
of these constituents is projected from individual lexical entries.
However, this shift of emphasis makes it increasingly difficult to
view the lexicon as a simple list of lexical entries (like a conventional
dictionary) since this organisation does not support generalisation
about classes of lexical items.

\subsection{Lexical Grammar}

Chomsky (1970) discussed the problem of nominalisation for generative
(transformational) grammar and proposed a new theory of grammar in
which lexical redundancy rules rather than transformations were used
to express the relationship between a verb and a morphologically
derived nominal. Chomsky's arguments for this move were, in part,
concerned with the restriction of transformational operations, but
also with the idiosyncratic properties of many derived nominals; that
is, the rules which relate derived nominals to their corresponding
verbs are often semi-productive because, for example, although the
morphological operation involved in the derivation of the nominal is
regular, its meaning is specialised and unpredictable ({\it revolve,
revolution}). Work following  this influential paper has tended to
emphasise the semi-productive or lexically-governed nature of many
other phenomena and to make greater use of formal devices, such as
lexical redundancy rules, which serve to relate lexical entries and
enrich the structure of the lexicon. The introduction to Moortgat et
al. (1980)  provides a detailed account of these developments.

One landmark in the development of lexical grammar is the account of
subcategorisation and coordination developed within Generalized Phrase
Structure Grammar (GPSG, Gazdar {\it et al.}, 1985). Simplifying somewhat,
lexical items are
subcategorised via a feature which effectively indexes them to
specific Phrase Structure (PS) rules which introduce their appropriate
syntactic arguments as phrasal sisters; for example, the PS rules in
\exnum{+1}{a,b} are appropriate expansions for transitive verbs which
take a noun phrase (NP) object and for verbs taking infinitival verb
phrase (VP) objects, respectively.
\begin{ex}
\begin{subex}
VP $\rightarrow$ V[Subcat 2] NP[Acc]
\end{subex}
\begin{subex}
VP $\rightarrow$ V[Subcat 6] VP[Infin]
\end{subex}
\begin{subex}
X $\rightarrow$ X X[Conj and] 
\end{subex}
\end{ex}
Verbs of each type will be listed in the lexicon with appropriate
values for the Subcat feature, so {\it kill} would have the value 2,
whilst {\it try} would be 6. GPSG also posited very general PS rule
schemata for coordination of which a simplified example is given in
\exnum{0}{c}, for binary conjunction where X ranges over syntactic
categories. These rules interact together to predict the
(un)grammaticality of the examples in \exnum{+1}{} in a simple and
intuitive way.
\begin{ex}
\begin{subex}
Kim [$_{VP}$ [$_{VP}$ killed Sandy] [$_{VP}$ and tried to leave]] 
\end{subex}
\begin{subex}
Kim killed [$_{NP}$ [$_{NP}$ Sandy] [$_{NP}$ and her friend]] 
\end{subex}
\begin{subex}
Kim tried [$_{VP}$ [$_{VP}$ to pay] [$_{VP}$ and to leave]] 
\end{subex}
\begin{subex}
*Kim killed [$_{?}$ [$_{NP}$ Sandy] [$_{VP}$ and to leave]]
\end{subex}
\end{ex}
Thus, coordination is constrained by lexical projections in the form
of PS rules indexed to lexical (sub)classes via the Subcat feature.
However, the search for a unified account of local grammatical
agreement lead Pollard (1984) to propose a framework in which the
Subcat feature takes as value an ordered list of syntactic categories
and PS rules are replaced by a very general PS schema which combines a
lexical item with the topmost category of its Subcat list and creates
a new phrasal category with a `popped' Subcat list. In this framework,
the (simplified) lexical entry for a transitive verb would be
\exnum{+1}{a} and the PS schema would construct the analysis outlined
in \exnum{1}{b} (where I abbreviate the Subcat feature to its value).
\begin{ex}
\begin{subex}
kill : V[Subcat $<$NP[Acc] NP[Nom]$>$]
\end{subex}
\begin{subex}
$[_{V[<>]}$ Kim [$_{V[<NP[Nom]>]}$ killed him]]
\end{subex}
\end{ex}
Agreement of features such as person, number or case (illustrated
here) can be straightforwardly and uniformly enforced by encoding such
features on categories specified on the Subcat list of lexical items.
Within this framework, the syntactic component has been drastically
reduced since individual PS rules have been replaced by a single
schema which builds constituents according to the specifications of
Subcat lists projected from lexical entries. This schema will,
however, interact with that for coordination in \exnum{-2}{c} to
cover the examples illustrated in \exnum{-1}. 

One apparent disadvantage of this radically lexical approach to
grammar is that it appears to involve considerable redundancy and loss
of generalisation if the lexicon is organised as an unrelated list of
entries; for example, \exnum{0}{a} encodes the information that the
subject of {\it kill} combines with it after its object and that the
subject must be nominative. However, these facts generalise to all
verbs of English whilst the fact the {\it kill} takes only one object
generalises to all transitive verbs. Further developments of syntactic
theory have reinforced the trend to relocate information in the
lexicon (e.g. Pollard \& Sag, 1987; Steedman, 1985; Zeevat {\it et al.},
1987). Flickinger {\it et al.} (1985), Pollard \& Sag (1987) and others 
propose that the lexicon be represented as an inheritance hierarchy
in which information common to a class of lexical items is inherited by 
all its subclasses; thus, the information that verbs take nominative
subjects is associated with the verb class node and inherited by all
subclasses, such as transitive verbs. These proposals enrich the struture
of the lexicon in a fashion which allows generalisations about lexical
(sub)classes to be expressed economically.

\subsection{Lexical Semantics}

Most formal semantic theories have concentrated on the problems of
compositional rather than lexical semantics; that is, the construction 
of sentence meaning from the meaning of constituent words and phrases.
Many lexical theories of grammar are monostratal, admitting
only one level of syntactic representation (e.g. Gazdar {\it et al.}, 1985). 
These theories associate semantic representations with each syntactic
constituent in some fashion; for instance, early versions of GPSG paired 
a semantic rule with each syntactic PS rule, which built the semantics
of the left-hand mother category out of the semantics of each right-hand
daughter category. Within the more radically lexical theories, the
compositional semantics of (at least) lexical items and their syntactic
arguments is also relocated in the lexicon. Such theories are often
called sign-based because they formally instantiate Saussure's
concept of a linguistic sign as the (arbitrary) association of sound,
form and meaning (e.g. Pollard \& Sag, 1987). In a sign-based theory,
the lexical entry for a transitive verb will include the information
that the semantics of the subject and object syntactic arguments
function as the semantic
arguments of the predicate associated with the verb. This information
too generalises to all transitive verbs, but locating it in the lexicon
allows the same very general schema which is used to construct the
syntactic representation of phrases and clauses to also build up the
semantic representation in tandem. (An example of a lexical entry of this
type is given in the next section.)

Recently, there has been renewed interest and research on the meaning of
words themselves and, in particular, work on how lexical semantic 
properties affect both syntactic behaviour and compositional semantic
interpretation. To take just two examples: Levin (1988, 1990) has
argued that it is not adequate to simply list alternative syntactic
realisations of verbs in terms of separate lexical entries with distinct
values for the Subcat feature or its equivalent, because such alternate
realisations are partly predictable on a semantic basis and may have
semantic consequences. For instance, change of possession verbs such as
{\it give} often undergo the dative alternation illustrated in 
\exnum{+1}{a,b}.
\begin{ex}
\begin{subex}
Kim gave the beer to Sandy
\end{subex}
\begin{subex}
Kim gave Sandy the beer
\end{subex}
\begin{subex}
Kim slid the beer to Sandy / the table edge
\end{subex}
\begin{subex}
Kim slid Sandy / *the table edge a beer
\end{subex}
\end{ex}
Change of position verbs such as {\it slide}, however, can only undergo
the dative alternation if they can be interpreted as conveying a change 
of possession, as \exnum{0}{d} illustrates. Pustejovsky (1989a,b)
discusses examples such as \exnum{+1}{a,b,c} in which {\it enjoy} conveys
an identical relationship of pleasurable experience between the 
experiencer subject and an event denoted by the verb's object of 
which the experiencer is agent.
\begin{ex}
\begin{subex}
Kim enjoys making films
\end{subex}
\begin{subex}
Kim enjoys film-making
\end{subex}
\begin{subex}
Kim / Coppola enjoyed that film
\end{subex}
\end{ex}
Positing separate lexical entries on the basis of the differential
syntactic realisations of {\it enjoy} with either a NP or progressive
VP object fails to capture the semantic relatedness of these examples;
thus, in \exnum{0}{b} we need to account for the manner in which the
implicit agent of the event-denoting NP {\it film-making} is associated
with Kim, whilst in \exnum{0}{c} we must explain the mechanism which 
allows {\it that film} to denote an event of Kim watching (or Coppola
making) a film. Pustejovsky refers to this latter process as logical
metonymy since he argues that {\it enjoy} coerces its artifact-denoting 
NP object into an event of some type, whilst the lexical semantic
representation of the NP itself determines the broad nature of the 
understood event --- compare {\it Kim enjoyed a beer}.

Work of this type on lexical semantics, as well as much other research
on, for example, aksionsart (Sanfilippo, 1990), or the argument structure of
derived nominals (Grimshaw, 1990), poses a considerable challenge to 
lexical grammar and theories of lexical organisation. Such theories
must demonstrate how lexical semantic information affects patterns
of syntactic realisation and also the process of compositional interpretation.

\subsection{The Lexical Representation Language}

Theories of grammar must be expressed in a formal language with an
appropriate syntax and semantics. As theories have become more lexical,
the focus of such metatheoretical work has also shifted to the lexicon.
I will refer to the language in which the lexicon is expressed as the
lexical representation language (LRL).
Most monostratal and lexical theories of grammar treat syntactic 
categories as feature structures (FSs) with unification as the mode of
combination of information in FSs. Unification is a form of bi-directional
pattern matching which is used extensively in theorem proving and logic
programming and which owes its introduction into linguistic theory as
much to work in NLP (Kay, 1974; Shieber, 1984) as to work on theories
of lexical grammar. A FS for the transitive verb {\it kill} is given in
\exnum{+1}{} which could constitute (the syntactic and semantic part of)
its lexical entry in a theory of the type outlined in the last two 
sections. This FS is displayed in attribute-value matrix notation in which 
coindexing indicates token identity of subparts of the FS and bold face
expressions give the type of each (sub)-FS (see 4.4 below).
Unification of two FSs, if defined, produces a new FS in which the
information from both is monotonically combined. Shieber (1986) and
Kasper \& Rounds (1990) provide detailed introductions to
unification-based approaches to grammar and to the syntax and
semantics of the formalism.

The FS in \exnum{1}{} is simple by comparison to that which would be
required in a realistic, wide-coverage grammar, yet already it encodes
a large amount of information much of which is true of other
transitive verbs. The LRL should allow the aspects of this FS common
to all transitive verbs to be expressed just once rather than repeated
in each individual lexical entry. Shieber (1984) describes the use of
lexical templates to name and define subparts of FSs common to classes
of lexical items and to abbreviate entries themselves to lists of
template names which would be expanded to full FSs on demand. This
approach compacts lexical entries and allows the expression of certain
generalisations, particularly as templates can be embedded within
other template definitions.  However, templates are abbreviatory
devices which do not enforce any specific organisation of the lexicon
and which do not strongly constrain the featural content of FSs.
Moens {\it et al.} (1989) present a typed FS system 
and Carpenter (1990, 1991) develops
a scheme in which FSs are typed and structured via a partial order on
types. Thus, a type places appropriateness conditions on a class of
FSs and these conditions must also be satisfied by FSs which are
subtypes of that type. The type system can be used to define an
inheritance hierarchy in which FSs `lower' in the type hierarchy are
monotonically enriched with information derived from appropriateness
conditions.
\begin{ex}
{\scriptsize
$\avmplus{\att{strict-trans-sign} \\
          \attvaltyp{orth}{kill} \\
          \attval{cat}{\avmplus{
                       \attval{result}{\avmplus{\att{strict-intrans-cat}\\
                                                \attvaltyp{result}{sent-cat} \\
	                                        \attval{active}{\avm{\att{np-sign} \\
		                                                        \attval{sem}{\ind{1}}}}}}\\
                       \attval{active}{\avm{\att{np-sign} \\
		                            \attval{sem}{\ind{2}}}}}}\\
          \attval{sem}{\avmplus{\att{strict-trans-sem} \\
	                         \attval{ind}{\ind{3}\myvaluebold{eve}} \\
          		         \attvaltyp{pred}{and} \\
                      		 \attval{arg1}{\avm{\att{verb-formula} \\
                                	            \attval{ind}{\ind{3}} \\
                                                    \attvaltyp{pred}{kill} \\
                                                    \attval{arg1}{\ind{3}}}} \\
	                         \attval{arg2}{\avmplus{\att{binary-formula} \\
                                             \attval{ind}{\ind{3}} \\
                                             \attvaltyp{pred}{and} \\
                                             \attval{arg1}{\ind{1}\avmplus{\att{p-agt-formula} \\
                                                                \attval{ind}{\ind{3}} \\
                                                                \attvaltyp{pred}{p-agt} \\
                                                                \attval{arg1}{\ind{3}} \\
                                                                \attvaltyp{arg2}{obj}}} \\
                                             \attval{arg2}{\ind{2}\avmplus{\att{p-pat-formula} \\
                                                                \attval{ind}{\ind{3}} \\
                                                                \attvaltyp{pred}{p-pat} \\
                                                                \attval{arg1}{\ind{3}} \\
                                                                \attvaltyp{arg2}{animate}}}}}}}}$}
\end{ex}
It is possible to define several notions of lexical rule within this
framework. Shieber (1984) describes a very general mechanism which can
define arbitrary mappings between two FSs, whilst Copestake \& Briscoe
(1991) propose to constrain this somewhat by placing further 
conditions on `input' and `output' FS expressed in terms of a type system.
Bresnan \& Kanerva (1989) and Bresnan \& Moshi (1989) make
use of a more restrictive notion of lexical rule which only allows
monotonic enrichment of underspecified lexical entries.

Many researchers have proposed to augment the logic of FSs with further
operations such as negation, disjunction, conditional implication and
default inheritance (e.g. Pollard \& Sag, 1987; Carpenter, 1990, 1991; Russell
{\it et al.}, 1990; Evans \& Gazdar, 1990). Unconstrained addition of 
such operations
considerably complicates either the computational tractability or the semantic 
interpretation of the formalism, or both, and there is still debate over
which such extensions are linguistically and formally desirable (see e.g.
the papers in Briscoe, Copestake \& de Paiva, 1991). In a wider context,
it is not clear that a restriction to unification-based formalisms is
either tenable or desirable; Pustejovsky (1989a), for example, makes use
of mechanisms drawn from general purpose knowledge representation 
languages developed within artificial intelligence to characterise
certain lexical semantic processes. Hobbs {\it et al.} (1987) argue that 
capturing the semantics of lexical items requires a first-order
logical representation and mechanisms which support abductive as well
as deductive inference. Undoubtedly the inferential processes involved
in language comprehension extend beyond the limited mechanisms provided
within unification-based formalisms, however, it is not clear yet whether
lexical operations {\it per se} require them.

\section{The Lexicon and Natural Language Processing}

Natural language processing applications, ranging from superficial text
critiquing through to machine translation, require knowledge about words.
In most cases, to be practical and habitable such systems must be
furnished with a substantial lexicon covering a realistic vocabulary and
providing the kinds of linguistic knowledge required for the application. 
Even apparently simple applications often require quite diverse lexical
knowledge; for instance, it is straightforward to make a case for a
spelling checker to be able to utilise orthographic knowledge, 
phonological knowledge (to deal with confusions caused by homonymy), 
morphological knowledge (if only to allow access to the lexicon),
syntactic knowledge (to allow recognition of errors creating legal
words in syntactically illegal contexts), crude semantic knowledge (to
select specialised term banks), and so forth. In a survey
of systems covering a range of tasks, Whitelock {\it et al.} (1987)
found that the average lexicon size was 1500 lexical items (which fell 
to 25 if one large MT system was discounted). The Core Language Engine
(Alshawi {\it et al.}, in press), a state-of-the-art
unification-based parsing and generation system for English intended as
a generic front-end for computer systems, has a lexicon of about
2000 words (Carter, 1989).

One reason why the lexical capabilities of NLP systems has remained weak
is because of the labour intensive nature of encoding lexical entries. If
we assume that the task of developing an adequate `core' lexicon is 
equivalent to that of developing a conventional advanced learners'
dictionary from scratch (containing typically 50,000 entries), then the
labour required runs into hundreds of person / years.
Furthermore, the {\it Oxford English Dictionary} contains 500,000
entries (and is still unlikely to cover all the words which will be
found in naturally-occurring input (Walker \& Amsler, 1986).
As the sophistication and coverage of other 
aspects of NLP systems increases, so the need to address this problem
becomes more urgent. Since the resources required for manual development of
lexicons are typically not available, some NLP researchers have turned to 
the machine-readable versions of conventional dictionaries (MRDs) as a 
highly-structured and substantial source of lexical information. However,
there are disadvantages to this approach because MRDs present information
in  a manner which relies on the linguistic skills and background 
knowledge of the user, whilst implementable theories of the lexicon can
make no such assumptions. Therefore, following the lead of lexicographers
themselves (e.g. Sinclair, 1987; Summers, 1990), others have opted to 
attempt to 
(semi-)automatically acquire lexical information from naturally-occurring
textual and transcribed spoken corpora, whilst in several recent
projects the emphasis has been placed squarely on manual development of
lexicons by large teams of researchers.

\subsection{Exploiting Machine-Readable Dictionaries}

There are a wide variety of MRDs which are made available by
dictionary publishers in formats ranging from typesetting tapes
through to quite sophisticated databases. The ideal resource from the
perspective of NLP research would be a fully explicit advanced
learners' dictionary organised as a database which has undergone very
systematic error checking.  These sources tend to assume less
linguistic competence on the part of the user and, therefore, supply
more grammatical information, use restricted defining vocabularies,
and so forth, but MRDs such as the {\it Longman Dictionary of
Contemporary English} (LDOCE), the {\it Oxford Advanced Learners'
Dictionary} (OALD) or {\it Collins Cobuild English Language
Dictionary} only approach this ideal and as advanced learners'
dictionaries are rare for languages other than English, considerable
energy and some debate has been devoted to the problems of converting
MRDs into databases, recognising and compensating for errors or
inadequacies, and so forth. Early work tended to focus on a single MRD
(e.g. Amsler, 1981), whilst recent efforts have attempted to merge and
integrate information from more than one source and sometimes more
than one language (e.g.  Byrd {\it et al.}, 1987). There are now
several alternative and well-developed approaches to deriving a
lexical database (LDB) from a typesetting tape (e.g. Boguraev {\it et al.}, 
1991)
offering the ability to access classes of lexical items and entries on
the basis of any of the information contained in one or more converted
sources.

The extraction of substantial quantities of information from a LDB is
in one sense trivial because once a MRD has been converted it is easy,
for example, to list all the verb senses which contain the word {\it cause}
in their definition. However, the usefulness of this activity can only
be evaluated relative to theoretical proposals concerning how such
information might be utilised and represented in the lexicon. More
abstractly, the contents of the LDB retain whatever implicit semantics
was intended by the lexicographers, whilst the utilisation of this
information requires a demonstration that it can be related to a formal
theory of the relevant domain and represented in an appropriate LRL. For
example, there is no guarantee that two MRDs will use a label like `vt'
(transitive verb) in the same manner, so a demonstration of the utility
of this information requires that we relate it to a theory of syntax
and subcategorisation of verbs, represent the information in the LRL
provided by this theory and provide evidence that correct predictions
are made about the lexical items involved. For instance, the theory
concerned might predict via a regular lexical operation that transitive
verbs can undergo passive, but the implicit definition used by the
lexicographers might well not have used this as a criterion of 
transitivity. In this case, a direct mapping of `vt' into the LRL will
result in examples such as {\it Kim was resembled} or {\it 5 pounds was
weighed by the book} being parsed or generated. This kind of problem is
more common than might be expected, precisely because of the static
character of a printed dictionary or LDB, as compared to the dynamic nature
of a LRL which incorporates a theory of valid lexical operations. I refer 
to the end result of this process of correctly mapping information from  
a LDB into the LRL as a lexical knowledge base (LKB). A LKB is, in effect,
an instantiated LRL, the ultimate goal of research in this area.

To date, the most successful work on the construction of LKBs from MRD
sources has been based on utilising more codified information, such as 
headword orthography, part-of-speech codes, grammatical codes and
pronunciation fields; for example, Church (1985) discusses the use of
pronunciation fields in the construction of an LKB for text-to-speech
synthesis and Boguraev \& Briscoe (1989) contains several papers which
evaluate and describe the use of LDOCE grammar codes in a LKB with
subcategorised verbs, adjectives and nouns. Work on lexical semantics
has for the most part resulted in more codified and accessible LDB
representations rather than in genuine LKBs; in part this is because of
the lack of a theoretical consensus on most aspects of lexical semantics,
but it also reflects the greater difficulty of extracting useful 
information from dictionary definitions intended for human consumption.
Various pattern matching and parsing tools have been constructed for
recognising genus terms in definitions and the syntactico-semantic
relationship between genus and differentiae (e.g. Amsler, 1980; Alshawi,
1989; Vossen, 1990a). Using such tools the information from
dictionary definitions has been structured in various ways to create,
for instance, hierarchically structured taxonomies of genus terms, but
often the senses of the genus terms have remained unresolved and where
they have been resolved this has been in terms of the source dictionary's
sense distinctions (e.g. Copestake, 1990; Guthrie {\it et al.}, 1990).

\subsection{Manual Development of Large Lexicons}

Several researchers have argued strongly that MRDs are inappropriate
sources of information for a LKB because they are too far removed from
any adequate theory of the lexicon (e.g. Gross, 1984). A larger number
would maintain that the overhead of converting MRD sources into LDBs is 
too great given the often unreliable and unsystematic nature of the
information that can be derived from them. Experience in the past with
manual creation of large lexicons is difficult to evaluate, for instance
the Linguistic String Project (Sager, 1981) is said to have developed a
lexicon of about 10,000 word forms manually but no analysis of the accuracy
of the entries or resources required to develop it is available. Recently,
several projects (EDR, Japan, Uchida, 1990; GENELEX, Esprit, Normier \&
Nossin, 1990; MULTILEX, Esprit, McNaught, 1990) have begun in which the
intention appears to be to develop quite substantial lexicons or, at 
least, LDBs primarily manually. 

The EDR project will cost 100 million
US dollars, run for 9 years and intends to develop bilingual resources
for English and Japanese containing 200,000 words, term banks 
for 100,000 words, and 400,000 concepts defined in terms of a 
semantic network. Although development will be assisted by corpus analysis
and by software support, the primary method of creating entries will be
manual encoding by teams of researchers. The EDR project will undoubtedly
advance the state-of-the-art in the production of `electronic' dictionaries
or in my terms LDB, but it is unclear to me whether the project will
produce a LKB. This LDB will be of use to researchers
in NLP, but I doubt that it will form a satisfactory base for direct
deployment in most applications. In the descriptions of the project 
available to me it
appears that the emphasis is entirely on achieving substantial coverage
and not on the many theoretical issues which need addressing before it
will be possible to develop a genuine LKB. In addition this project, as 
with the development of a conventional dictionary, will be prey to problems
of inconsistency, errors of commission, and so forth, created by the use
of teams of manual encoders. In this respect, it is surprising that there
appears to be no link with a dictionary publisher and no attempt to
exploit the considerable experience of lexicographers in the management
of such projects (Atkins, 1990).  

\subsection{The Role of Corpora} 

Summers (1990) reminds us that Dr. Johnson's great dictionary of English 
published in 1755
was based on quotations from literature, rather than introspection about
word meaning. The availability of vast machine-readable corpora (e.g. Liberman,
1991) and of software tools for analysing and selecting subsets of
such corpora makes the task of empirical lexicography considerably easier.
The Cobuild dictionary was developed by a team of lexicographers using
a written corpus of 6 million words supported by software tools such as
concordancers, a database and editors (Clear, 1987). Cobuild is, in
many respects, superior to its predecessors, such as in recognising senses
of words that had slipped through the introspective net of lexicographical
tradition. Nevertheless, the
Cobuild project was very resource intensive, because the task of analysing 
large quantities of unsorted citations of particular word forms in context
is complex and time consuming (e.g. Fillmore \& Atkins, 1991). 

Recently, some NLP researchers (e.g. Brent, 1991; Hindle \& Rooth, 1991) 
have advocated the use of corpora for automatic acquisition of lexical
information. This raises the possibility of, at least, semi-automatic
construction of lexicons directly from corpora. However, many fundamental
problems in NLP will need to be solved before this highly desirable
prospect becomes practical, because the extraction of many types of
information from corpora usually presupposes the capability to automatically
analyse the raw text in various ways. Furthermore, achieving this 
capability will itself involve developing substantial lexicons.
For example, it would be useful to acquire
information about the relationship between alternative senses of a word
and its syntactic realisation, but how would one recognise an alternative
sense or the syntactic and semantic relationships between it and the
words and phrases in the surrounding context? This appears to require
at least a theory of sense distinctions and a parser capable of phrasal
analysis and of discriminating arguments from adjuncts which, in turn,
implies the existence of a lexicon with reliable information about
subcategorisation.
 
Nevertheless, robust techniques exist for some types of corpus
analysis, such as part-of-speech tagging (e.g. de Rose, 1988) or
derivation of surface collocations (e.g. Church \& Hanks, 1990), and
as work on statistical and robust approaches to corpus analysis
continues more complex analysis will become reliable enough for
routine use; for example, phrasal parsing (e.g. Hindle \& Rooth,
1991). And already these techniques allow the derivation of
information which in some respects surpasses that available from MRD
sources; for example, information about the frequency of words
occurring as different parts-of-speech. It seems both likely and
desirable that corpus analysis will play a greater role in the
acquisition of lexical information, but unlikely that this approach
will supplant others or render more theoretical work irrelevant.

\section{ACQUILEX}

In this section, I will describe some of the research undertaken as part
of the ACQUILEX project in order to make some of the earlier discussion
more concrete. The goal of ACQUILEX is to demonstrate that information
can be usefully extracted from multiple MRD sources in a resource efficient
fashion in order to (eventually) construct a multilingual LKB for use in 
NLP applications. Work on ACQUILEX can be divided into two broad areas:
firstly, the development of software tools and a database framework to
facilitate the mapping from LDB to MRD, and secondly, the adoption or
development of theoretical accounts of aspects of the lexicon and the
subsequent construction of illustrative LKB fragments semi-automatically
from LDBs using a further set of software tools design to integrate, 
transform and enrich LDB information.

\subsection{Mapping from MRD to LDB}

\subsubsection{Functionality of the LDB}

The LDB system developed at Cambridge implements the two-level
dictionary model (Boguraev {\it et al.}, 1991).  In the two-level
model, the source dictionary is the primary repository of lexical
data, and, separately from the dictionary source, sets of interrelated
indices encode all statements about the structure and content of the
data held in the dictionary.  Thus all the information associated with
the dictionary source is preserved, but structural relationships are
also expressed.  Since new indices can be added incrementally it is
unnecessary to try and establish all the possible relationships from
the start. This set-up is appropriate for a highly-structured but
primarily textual object which is continuously having further
structure imposed on it as more information is extracted / made
explicit. The LDB can support the model of the Common Lexical Entry
described in Calzolari {\it et al.} (1990) both in terms of the
graphical presentation and underlying representation of MRD entries.
The LDB system is used throughout the ACQUILEX project and by a number
of other research groups, and is described in detail in Boguraev {\it
et al.} (1989) and Carroll (1990).

The mounting of a new
machine-readable dictionary in the LDB
can be divided into four stages:
\begin{enumerate}
\item Transforming the MRD source into a suitable format, while preserving
information.  The complexity of this stage depends on the format of
the tape supplied by the publisher.
\item Defining what the
indices are and how they are to be extracted from entries.
\item Defining the format of
queries that the user can construct (that is, the possible attributes
and their hierarchical organisation), and how these queries correspond
to the indices created for the dictionary.
\item
Telling the system to create permanent files on disc holding the
indices, and the menus for the graphical query interface.
\end{enumerate} 
In fact, two types of indices are created: one type on the
contents of headword fields (and also optionally on internal entry
sequencing information on the typesetting tape), enabling access to
entries via their headwords (similar to the traditional way of using
printed dictionaries); the other type based on the contents of
entries, allowing the dictionary to be queried, and entries to be
retrieved from it, on the basis of elements and their relationships
within entries, rather than just by headword.

An LDB query consists of a hierarchical collection of attributes with
associated values; for example the query
\begin{verbatim}
   [[syn
      [gcode T1]]
    [sem
      [word show]]]
\end{verbatim}
has two attributes at the top level: `syn' and `sem'; the
attribute `gcode' is beneath `syn' with value `T1',
and `word' beneath `sem' with value `show'. 
When looking up a query, the LDB, by default, computes the answers in
a sense-based (rather than an entry-based) fashion; that is, it
returns just the senses which satisfy the query, not the whole entry
(unless of course all the senses in the entry satisfy it).

We also use the LDB to store information which is derived from the MRD
source, but is not sufficiently analysed to make it part of a LKB.
For example, the results of analysing the definitions, as described in
the next section, are stored in the LDB as a {\em derived dictionary},
with entries which are in direct correspondence to the source dictionary.
The LDB allows the user to apply a single query to two
or more such corresponding dictionaries simultaneously.

\subsubsection{Analysing definitions}

It is not currently possible to produce an analysis of dictionary
definitions using a conventional parser, with a general purpose
grammar.
Two approaches taken within the ACQUILEX project, have
proved reasonably successful; using a robust, pattern-matching /
parsing tool, and development of a special purpose grammar for
definitions using a general purpose parser.

The flexible pattern matching / parsing tool (FPar) which is integrated 
with the Cambridge LDB is based on the system described in
Alshawi(1989).
This uses a grammar in the form of a hierarchy of patterns; the most
general patterns providing some interpretation of a text even if the more
specific and detailed ones fail.  As an example of an (atypically detailed)
use of FPar on LDOCE:
\begin{description}
\item [launch$^3$] a large usu. motor-driven boat used for carrying
people on rivers, lakes, harbours, etc.
\begin{verbatim}
((CLASS BOAT) (PROPERTIES (LARGE))
  (PURPOSE
    (PREDICATION (CLASS CARRY) (OBJECT PEOPLE))))
\end{verbatim}
\end{description}
FPar has also been
applied to the Spanish VOX dictionary (Rodriguez {\it et al.}, 1990), which 
is a much larger dictionary than LDOCE, and does not make use of
a restricted defining vocabulary.  Rather than attempting to build
grammars which would work for the whole of VOX, different pattern
hierarchies have been developed for different semantically-related groups of 
definitions.

In contrast, general purpose parsers have been used with special
purpose grammars.  Vossen (1990) describes work on LDOCE which has now
been extended to the Dutch Van Dale dictionaries; in Pisa, the IBM PLNLP
system has been used (Montemagni, forthcoming). In these approaches it
is necessary to develop quite complex specialised grammars both to
deal with the sublanguage of definitions and to reduce the number of
alternative analyses produced.

All these approaches allow the identification of genus phrases in definitions
with a good degree of accuracy (better than 95\% for Vossen's parser on
LDOCE noun definitions).
However some errors seem inevitable; for example in the LDOCE definition;
\begin{description}
\item[armadillo] ``a small animal native to the warm parts of the
Americas''
\end{description}
the genus was identified as {\it native} by both 
Vossen and Alshawi's analysers.
Analysis of the differentia is considerably more difficult and
currently all these systems give only partial information concerning
the syntactic and semantic relations which obtain between the phrases
of the differentia and the genus term. Since such systems are time
consuming to develop and tend to be specific to individual MRD
sources, in the longer term utilising probabilistic and robust parsing
techniques developed for corpus analysis seems desirable (see Briscoe
\& Carroll (1991) for a preliminary experiment with LDOCE definitions).

Although it is possible to produce a parsed structure which gives the
genus phrase and some indication of the relationships in the
definitions, this is not a disambiguated meaning representation, which
is what is needed to reason about the content of the
definitions to formally derive a lexical semantic representation in
the LKB.  Mapping from parsed definitions of this type to an LKB
representation involves a combination of heuristics and user
interaction, and is only possible within a context provided by the
LRL.
Copestake(1990) describes a program for producing taxonomies which can
be used to provide an inheritance structure for the LKB. This program
traces `chains' of genus terms through the dictionary; thus starting
from {\it animal} we might find {\it dog} and from {\it dog} {\it
terrier}, and so forth. The
essential steps in going from the definitions with the genus term
identified to a structure which can be interpreted in the formal
system are disambiguation of the genus term, and identification of the
type of relationship which holds between the genus term and the definiendum.
Consider the following LDOCE definition:
\begin{description}
\item[dictionary] {\bf 1} a book that gives a list of words in
alphabetical order, with their pronunciations and meanings.
\end{description}
Here {\it book} is the genus term, but it is essential to determine the
sense used; other definitions use {\it book} in other senses, for example:
\begin{description}
\item[Genesis] the first book of the Bible \ldots
\end{description}
User-specified heuristics are utilised to select the appropriate sense,
such as degree of word overlap between the definitions of the
senses of the genus and the current definition (see Copestake (1990)
for further details). Sense disambiguation is done semi-automatically
with the user confirming decisions concerning non-leaf nodes in the
hierarchy which emerges. Typically, it takes about 1 hour to create an
inheritance hierarchy for 500 word senses using LDOCE.

\subsubsection{Correlating MRDs}

Correlating MRDs is one way to overcome inadequacies, inconsistencies,
omissions and the occasional errors which are commonly found in a
single source (Atkins, 1991). Hopefully,
integrating information from several sources will provide the missing
information or allow errors to be detected.

To take an example, semantically-defined verb classes are
instrumental in providing an indication of lexically-governed
grammatical processes, such as the alternate syntactic realisations of the 
type discussed in 2.2, and should thus be included within a lexicon
which supplied adequate information about verbs. For example,
a verb such as {\it delight} should be specified as a member of the
class of verbs which express emotion, i.e. psychological verbs.  As is
well known (e.g. Levin 1990), these verbs can be further
classified according to the following parameters:
\begin{itemize}
\item affect is positive ({\it admire, delight}), neutral ({\it
experience, interest})  or negative ({\it fear, scare})
\item stimulus argument is realized as object and experiencer as
subject, e.g. {\it admire, experience, fear}
\item stimulus argument is realized as subject and experiencer as
object, e.g. {\it delight, interest, scare}
\end{itemize}
Unfortunately, conventional dictionaries do not supply
this kind of information with consistency and exhaustiveness,
so the technique of creating derived dictionaries where the
information contained in the MRD is made more explicit is unhelpful in
this case.  For example, one approach would be to derive a dictionary
where verbs are organized into a taxonomy by genus term, as in 4.1.2. 
Unfortunately, the genus of verb definitions is usually not specific
enough to supply a taxonomic characterization which would allow the
reliable identification of semantic verb classes.
In LDOCE, for example, the genus of over 20\% of verb
senses (about 3,500 verb senses) is one of 8 verbs {\it cause, make,
be, give, put, take, move, have}; many of the word senses which have
the same genus belong to distinct semantic verb classes.  This is not
to say that verb taxonomies are of no value; nevertheless, the
achievement of adequate results requires techniques which reclassify
entries in the same source MRD(s) rather than making explicit the
classification `implicit' in the lexicographer's choice of genus term.
Such a reclassification can be carried out by augmenting a
conventional learner's dictionary with thesaurus information; thesauri
provide an alternative semantically-motivated classification of
lexical items, and are, therefore, natural candidates for the task at
hand.

In the general case, the integration of information from distinct MRD
sources is probably going to remain an unsolved problem for quite some
time. This is simply because dictionaries seldom describe the same
word using the same sense distinctions. Consequently, the integration
of information from distinct MRD sources through simple word-sense
matches is likely to fail in a significant number of instances
(e.g. Calzolari \& Picchi 1986).
Indeed, Atkins \& Levin (1990) have suggested
that the task of mapping MRDs onto each other is so complex that the
creation of a complete `ideal' database, which provides a reference
point for the MRD sources to be integrated, may well be
an essential prerequisite. However, when dealing with MRD sources
which use entry definitions which are not too dissimilar, a
correlation technique based on word sense merging can be made to yield
useful results, given the appropriate tools. Although sense matching
across dictionaries in this case too is still prone to errors, there are
several reasons why the effort is still worthwhile. Firstly, the proportion of
correct sense matches across MRD sources is likely to
be high. Secondly, there are many instances in which an incorrect
sense-to-sense match does not affect the final result since the
information with respect to which a sense correlation is being sought
may generalise across closely related word senses. Thirdly, a close
inspection of infelicitous matches provides a better understanding of
specific difficulties involved in the task and may help us develop
better solutions or refine our criteria for sense discrimination.

Sanfilippo \& Poznanski (1991) investigated the plausibility of
semi-automatic sense correlations with LDOCE and the Longman Lexicon
of Contemporary English (LLCE) --- a thesaurus which was developed
from LDOCE and with which there is substantial overlap (although not
identity) between the definitions and entries. Their general goal in
developing an environment for correlating MRDs was to provide a
Dictionary Correlation Kit containing a set of flexible tools that
could be straightforwardly tailored to an individual user's needs,
along with a facility for the interactive matching of dictionary
entries. Entries are compared along a number of user specified
dimensions, such as headword, grammatical code, overlap of the base
form of content words in the definition, and so forth, and if an
experimentally determined threshold of similarity is found senses are
correlated, otherwise the user is asked to make the decision.  A trial
run with correlation structures derived for 1194 verb senses (over 1/5
of all verb senses in LLCE) yielded encouraging results, with a rate
of user interactions of about one for every 8-10 senses and a very low
incidence of infelicitous matches (below 1\%). We plan further experiments
with less closely related sources.

\subsection{Mapping from LDB to LKB} 

\subsubsection{Design of the LRL}

We chose to use a graph unification based representation language for
the LRL, because this offered the flexibility to represent both
syntactic and semantic information in a way which could be easily
integrated with much current work on unification grammar, parsing and
generation.  In contrast to DATR (Evans \& Gazdar, 1990), for example,
the LRL is not specific to lexical representation.  This made it much
easier to incorporate a parser in the LKB system (for testing lexical
entries) and to experiment with notions such as lexical rules and
interlingual links between lexical entries.  Although this means that
the LRL is in a sense too general for its main application, the typing
system provides a way of constraining the representations, and the
implementation can then be made more efficient by taking advantage of
such constraints.

Our typed FS mechanism is based on Carpenter's work on
the HPSG formalism (Carpenter 1990, 1991), although there are some
significant differences, for example, we augment the formalism with a default
inheritance mechanism.  This can be used to organise the lexicon in a
completely user-defined way, to allow morphological or syntactic
information to be concisely specified, for example, as has been done
with DATR and other systems.  However much of the motivation behind
our formalisation of default inheritance comes from consideration of
the sense-disambiguated taxonomies semi-automatically derived from
MRDs, which we are using to structure the LKB.  The top level of the
inheritance structure, which is too theory-dependent and abstract to
be automatically derived from MRDs, is, in effect, given by the type
system.  The notion of types, and features appropriate for a given
type, gives some of the properties of frame representation languages,
and allows us to provide a well-defined, declarative representation,
which integrates relatively straightforwardly with much current work
on NLP and lexical semantics. However, `lower' in the hierarchy the
nature of lexicographers' classifications forces a default framework
(see the next section).

The operations that the LRL supports are (default) inheritance,
(default) unification and lexical rule application.  It does not
support any more general forms of inference and is thus designed
specifically to support restricted lexical operations, rather than
general reasoning.  The type system provides the non-default
inheritance mechanism and constrains default inheritance.  We use
lexical rules as a further means of structuring the lexicon, in a
flexible, user definable manner, but lexical rules are also
constrained by the type system.  The type hierarchy defines a partial
order on the types and specifies which types are {\em consistent}.
Only FSs with mutually consistent types can be unified --- two types
which are unordered in the hierarchy are assumed to be inconsistent
unless the user explicitly specifies a common subtype.  Unification of
FSs is only defined if the meet of their types exists.
A full description of the LKB is given in Copestake (1991) and de
Paiva (1991).

One advantage of a typed LRL in a large collaborative project is that
once an agreed type system is adopted, the compatability of the data
collected by each site is guaranteed (there may of course be problems
of differing interpretation of types and features but this applies to
any representation).  In an untyped feature system, typographical
errors and so on may go undetected, and debugging a large lexical template
based system (see 2.3) can be extremely difficult; a
type system makes error detection much simpler.  Since a given FS has
a type permanently associated with it, it is also much more obvious how
information has come to be inherited than if templates are used.  The
type system can also be integrated with tools for semi-automatic analysis
of dictionary definitions, and initial work on this is described in
Rodriguez {\it et al.} (1991).  Typing provides the main means of
error checking when representing automatically acquired data.
Automatic classification of lexical entries by type, according to
feature information, can be used to force specification of appropriate
information. It remains to be seen whether this restricted LRL will prove
adequate for the representation of lexical semantics, but see Briscoe {\it
et al.} (1990) and Copestake \& Briscoe (1991) for proposals concerning
logical metonymy (see 2.2) and other phenomena which have been taken to
require more powerful formalisms.

\subsubsection{Creating semantic inheritance hierarchies}

We have extended the LRL with default inheritance, based on default
unification and restricted by the type system.
In outline, FSs may be specified as
inheriting information which does not conflict with their own
specification from other FSs.  Conflicting information
is simply ignored.  Since the parent FSs may themselves have been
defined as inheriting information, inheritance hierarchies can, in
effect, be created.  We are interested in the use of this mechanism to
structure lexical semantic information in particular.  In this case,
the inheritance hierarchy connects the parts of the lexical entries
which contain lexical semantic information, and we derive it from the
taxonomies described in 4.1.2.

For example {\it autobiography} and {\it dictionary} are 
found to be below `book 1 (1)' in the 
hierarchy semi-automatically derived from LDOCE,
and {\it lexicon} is found below {\it dictionary}.
Part of the lexical semantic structure specified for nouns which are
of semantic type {\bf artifact} is the `telic role' 
(Pustejovsky 1989a)
which indicates
the typical purpose of the object.  Thus in the feature structure
associated with book 1 (1) the telic role is instantiated to the
semantics of the appropriate sense of {\it read} (notated as
`read\_L\_1\_1').  Since this sense of book denotes a physical object, the
lexical entry also contains a feature {\sc physical-state}.
The following fragment of the representation shows the relevant
features:
\begin{quote}
book\_L\_1\_1 \hspace{0.5in}
{\scriptsize
   $\avmplus{\att{lex-noun-sign}\\
             \attvaltyp{orth}{book}\\
             \attval{rqs}{\avmplus{\att{artifact\_physical}\\
                  \attval{telic}{\avmplus{\att{verb-sem}\\
                                   \attvaltyp{pred}{read\_L\_1\_1}}}\\
                  \attvaltyp{physical-state}{solid\_a}}}}$}
\end{quote}
Lexical entries for {\it autobiography} and {\it dictionary} will be
automatically defined to inherit their semantic representation 
by default from book\_L\_1\_1, when their LKB entries are created.

Assuming that no 
conflicting information is specified for {\it autobiography} 
it will have the same values as
book\_1\_1 for both telic and physical-state.
However, 
{\it dictionary} should be specified 
as having  the {\sc telic} predicate (refer\_to\_L\_0\_2) which 
overrides that inherited from book\_L\_1\_ 1, giving the following
partial structure:
\begin{quote}
dictionary \hspace{0.5in}
{\scriptsize
   $\avmplus{\att{lex-noun-sign}\\
             \attvaltyp{orth}{dictionary}\\
             \attval{rqs}{\avmplus{\att{artifact\_physical}\\
                  \attval{telic}{\avmplus{\att{verb-sem}\\
                                   \attvaltyp{pred}{refer\_to\_L\_0\_2}}}\\
                  \attvaltyp{physical-state}{solid\_a}}}}$}
\end{quote}
Since {\it lexicon} is under {\it dictionary}
it will inherit the more specific value (refer\_to\_L\_0\_2)
for the telic role.

Attributes such as the telic role are currently being manually
associated with entries such as book\_L\_1\_1
which occur as non-leaf nodes in the
inheritance hierarchies.  This is cost effective since 
book\_L\_1\_1 directly or indirectly dominates over 100 other entries,
and in most cases the default inheritance is not overridden.

Vossen and Copestake(1991) and Vossen (1990b,c) discuss a number of
problems which arise in the derivation of inheritance structures from
genus taxonomies for Dutch and English nouns; these include, cross-linguistic
differences in choice of genus for certain classes, the semantics of
conjoined and disjoined genus phrases, weakly classifying genus terms,
and the relationship between genus and definiendum.

\subsubsection{Productive sense extensions}

Lexical rules are essentially just typed FSs which represent a
relationship between two lexical entries; the mechanism has been
described in Copestake \& Briscoe (1991) and I will not discuss it in
detail here.  Although lexical rule application is typically seen as a
way of deriving new lexical entries (but see Krieger \& Nerbonne,
1991) our formalisation of lexical rules also allows them to be viewed
as a way of describing a relationship between existing lexical
entries, and possibly augmenting the information contained in them.
Thus lexical rules provide a mechanism for linking lexical entries in
a way which can be defined by the user using the type system.  One
application is for the description of derivational morphology, another
is in the representation of sense extensions, such as the use of a
word which primarily denotes an animal to denote the flesh of that
animal (e.g. {\it lamb}).  By representing sense extensions with lexical
rules in the LRL we can allow the relationship between two word senses
extracted from an MRD to be described (e.g. lamb 1 (1) and lamb 1 (2) in
LDOCE) or, alternatively, fill in gaps in the source data (e.g. deriving a
sense meaning `haddock flesh' from the LDOCE entry for {\it haddock}
which provides only the animal sense). The lexical rule states that
a lexical entry of type animal and count noun can be mapped to one of
type animal flesh and mass noun. The blocking of lexical rule
application is well recognised in morphology (e.g. the existence of
{\it thief} makes the derivation {\it stealer} unlikely); thus, it is
interesting to note that the same phenomenon applies to this type of
sense extension -- the existence of {\it pork} makes the use of {\it
pig} to denote pork very marked.

Ostler \& Atkins (1991) give further examples of regular extensions of
this type which a dictionary, because of its static nature, is forced
to simply list (with consequent inevitable errors of omission).
However, by  mapping the data derived from an MRD source into a LKB
supporting lexical operations, it is possible to represent the
information in a fashion which does not proliferate sense
distinctions, which generalises beyond the source, and which
integrates with an account of the parsing process (see Copestake \&
Briscoe (1991) for further details).

\subsubsection{Recognising Verb Classes}

Through LDB queries, information is made available which specifies
properties of individual word senses (e.g.  orthography,
pronunciation, part of speech, predicate sense). This information can
be semi-automatically integrated with the information structures
associated with LKB types by defining a conversion function which
establishes correspondences between information derived through LDB
queries and values of the features specified by types. For example,
Sanfilippo (1991) shows how information for psychological verbs
derived through LDB queries can be related to a type system for
English verbs. The inheritance network of types represents detailed 
information about
syntactic and semantic properties of verb classes in a form which can
be easily tailored to suit requirements of specific NLP systems.

Using the results of the dictionary correlation study
briefly described in 4.1.3, LDB queries combining information
from LDOCE and LLCE were run which made it possible to individuate
members of six subtypes of psychological verbs:
\begin{ex}
\begin{tabular}[t]{lll}
{\sc stimulus argument} & {\sc experiencer argument} & {\sc illustrative example} \\
non-causative source & neutral, reactive, emotive & {\it experience} \\
non-causative source & positive, reactive, emotive & {\it admire} \\
non-causative source & negative, reactive, emotive & {\it fear} \\
neutral causative source & neutral, affected, emotive & {\it interest} \\
positive causative source & positive, affected, emotive & {\it delight} \\
negative causative source & negative, affected, emotive & {\it scare} 
\end{tabular}
\end{ex}
These subtypes were defined taking as parameters affect type (positive
or negative) and the syntactic realization of the experiencer and
stimulus arguments which was semantically characterized as follows:
\begin{quote}
Psychological verbs with experiencer subjects are `non-causative'; the
stimulus of these verbs can be considered to be a `source' to which
the experiencer `reacts emotively'. By contrast, psychological verbs
with stimulus involve `causation'; the stimulus argument may be
consided as a `causative source' by which the experiencer participant
is `emotively affected'. (Sanfilippo, 1991)
\end{quote}

The recognition of psychological verbs and their classification into
the six subclasses above was facilitated by the inclusion of LLCE set
identifiers and by the use of a lexicon from LDOCE containing
subcategorisation information (Carroll \& Grover, 1989) in LDB
queries. For example, whenever a
verb sense was associated with the information in
\exnum{+1}{} a base entry of type, {\tt strict-trans-sign} --- the LKB
type which describes syntactic and semantic properties of (strict)
transitive verbs --- was automatically created for that verb sense.
\begin{ex}
{\tt ((Cat V) (Takes NP NP) ...)}
\end{ex}
Using this technique, verb types were assigned to some 200 verb
senses; this assignment yielded a verb lexicon of 431 entries. The
entry below (specified in path notation) provides an illustrative example 
relative to one of the
six semantic varieties of psychological verbs taken into account.
\begin{ex}
\begin{verbatim}
experience L_2_0
STRICT-TRANS-SIGN
< cat : result : result : m-feats : diathesis > = NO-INFO
< cat : result : active : sem : pred > = P-AGT-REACTIVE-EMOTIVE
< cat : result : active : sem : arg2 > = (E-ANIMAL E-HUMAN)
< cat : active : sem : pred > = P-PAT-SOURCE-NO-CAUSE
< cat : active : sem : arg2 > = E-ABSTRACT
< lex-sign sense-id : sense-id dictionary > = "LDOCE"
< lex-sign sense-id : sense-id ldb-entry-no > = "12364"
< lex-sign sense-id : sense-id sense-no > = "0".
\end{verbatim}
\end{ex}
When loaded into the LKB, \exnum{0}{} will be expanded into a
fully-fledged representation for the transitive use of {\it experience};
by integrating word-specific
information provided by \exnum{0}{} with
the information encoded by the LKB type {\tt strict-trans-sign}. Thus,
although neither LDOCE, LLCE or the earlier subcategorised lexicon
contain all the information about psychological verbs defined in
Sanfilippo's type system, by using the conjunction of information
available from all three, it proved possible to effectively enrich this
information at the same time as mapping it into a formal representation.

\subsubsection{Towards a Multilingual LKB}

A goal of ACQUILEX is to demonstrate that an LKB
can be produced that usefully exploits various MRD sources and integrates
multilingual information.  The use of a common LRL with a common type
system, makes it possible to describe lexical entries in a shared
`metalanguage'.  This allows lexical entries to be compared, in
order to enrich a monolingual LKB or to provide multilingual
`translation links' to create an integrated multilingual LKB.
To ensure that information from monolingual sources for different languages
can be represented in a compatible way
Rodriguez {\it et al.} (1991) have developed a system for generating
LKB entries from analysed definitions which produces entries in the
common LRL.  In order to do this it is necessary to provide a way of
representing equivalences between attribute and value names extracted
by the parser, and the feature and type names in the LRL. 

Thus we have the beginnings of a semi-automatically
constructed LKB containing information from different sources and languages.
However, to create a genuinely integrated system, we need to link the senses
of the different sources.  The general problem with
automatically generating links between word senses, either
multilingually or monolingually, is that of sense selection.  In the
case of translation links, we are attempting to link entries derived
from two monolingual dictionaries.  Bilingual dictionaries, where they
do discriminate between senses, will typically use a different scheme
from the monolinguals (Van Dale's dictionaries being one exception), and the
translations given in bilinguals typically have no sense marking.
For example, the English---Spanish VOX dictionary published by Biblograf contains the
following entry:
\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}
However, 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. The approach we take is to attempt to choose
word senses which are appropriate translations of a source word sense,
by comparison of the information stored in their LKB entries. 
Copestake \& Jones (1991) have developed a general FS matching
utility, which can be used to find the best match between a set of
candidate LKB entries (where the candidates may have been identified using 
bilingual MRDs).  A
statistic is assigned to each potential match, with the magnitude of
the statistic proportional to the quality of match. These statistics
can then be compared to yield the most likely sense-to-sense mappings.

Although in the simplest (and commonest) cases, we can regard linked 
lexical entries
as translation equivalent, in general we have to allow for
such things as different argument ordering, differences in
plurality, differences in specificity of reference and `lexical
gaps', where a word sense in one language has to be translated by a
phrase in the other.  
Rather than attempt to generate information about translation
equivalence which can be directly used by a particular MT
system, we are attempting to describe the relationship between 
LKB word senses in such a way that the information could be
automatically transformed into the lexical component of a variety of
MT systems.
We represent the cross-linguistic relationships between lexical entries 
in terms of {\em
tlinks} (for translation link).  In general there may be a many-many
equivalence between word senses, but each possibility is represented
by a single tlink. The tlink mechanism is expressive enough to
allow the monolingual information to be augmented with translation
specific information, in a variety of ways.  As with other aspects of
representation in the LKB, the mechanism is very general; it is up to
the users of the system to define appropriate types to constrain it.

\section{Conclusions}

The previous sections have provided a brief and selective introduction to
work on the lexicon and selective description of research undertaken within
the ACQUILEX project. Space precludes proper discussion of many aspects of
lexical research, such as work on spoken language systems.
I will conclude by outlining the way in which work on the lexicon can
most productively proceed, but first I want to draw attention to two trends
which marr slightly the generally positive outlook for lexical research
which I hope has emerged from the discussion above.

\subsection{(Re)Usability}

Mention of reusability has become so common in work on the lexicon
recently that no paper in this area can ignore the issue completely.
Nevertheless, I believe that the ideas that lie behind the slogan are
sufficiently confused that there is a danger that what began as a
laudable goal will become counter-productive. It is clearly desirable
that if considerable effort is devoted to the development of
substantial LDBs and LKBs that the results of this effort should, as
far as possible, outlast the latest theoretical fashions and current
generation of NLP systems, and should be available generally within
the research community. To this end, the development of standards for
the interchange of data through the Text Encoding Initiative (e.g.
Amsler \& Tompa, 1988) and the creation of a repository, in the form of
the Consortium for Lexical Research (Wilks \& Guthrie, 1990), for the
distribution of such material, as well as software tools for creating
LDBs, analysing corpora and the like, is to be welcomed as long as
such initiatives are not allowed to overshadow the lexical research
itself. However, the goal of developing `generic', `theory-neutral' or
`polytheoretical' lexical resources is illusory, and potentially
harmful if it is interpreted as a reason to avoid theory and
concentrate on the creation of large LDBs.

In the discussion above, I have tried to draw a very definite and precise
distinction between a LDB and a LKB. In my terms, the latter represents
a theory of the lexicon and is, therefore, a body of information couched
in a notation -- the LRL -- which has a formal and explicit syntax and
semantics, and which supports lexical operations which perform valid
transformations on this information. An LDB, on the other hand, contains
information couched in a far looser and varied syntax, typically with an
implicit semantics. In terms of both usability and reusability, at least
within NLP, it is clear that a LKB will be superior. It is usable precisely
because it will be highly theory-dependent and, therefore, make clear and
mechanically derivable predictions, and it will be reusable for the same
reasons: if we have two LKBs instantiating different theories, one superior
in size, the other in theoretical adequacy, it is likely that we will have
enough understanding of the relationship between them to carry over the
useful information from the former to the latter. Ingria (1988) describes
just such a case of the largely automatic creation of a lexicon 
containing syntactic information for a new parsing system from the lexicon
associated with an earlier and less adequate one. The use of the derived
lexicon containing syntactic subcategorisation information in the 
development of a LKB providing
a more adequate characterisation of psychological verbs described in
4.2.4 is another example. On the other hand, the
relationship between a LDB and a LKB or between two LDBs is likely to be
much more difficult to specify, because of the implicit nature of the
semantics of the information contained in a LDB. Nevertheless, a LDB is
not `theory-neutral' --- any description language constitutes an 
interpretation of some sort --- rather insofar as the semantics of its
description language is obscure and ill-understood, a LDB is not so much
`polytheoretical' or `theory-neutral' as just vague, and thus of diminished
utility. None of this should be surprising, given that it has been a
commonplace of the philosophy of science for most of this century that
all observation and description is theory-laden (e.g. Hanson, 1958). What
is perhaps more pertinent is that McNaught (1990) draws similar conclusions
in the context of a description of the MULTILEX project, a
major goal of which is to define techniques for creating reusable
lexical resources. (Re)Usability requires a greater emphasis on 
theoretical issues, particularly in the area of lexical semantics, not
de-emphasis in favour of a large data gathering excercise conducted in
a relative theoretical vacuum.

\subsection{Lexicography}

The predominant role of the bulk of lexicographers in research on the 
lexicon within NLP and linguistics has been to supply the latter
with extremely useful sources of information, whether in printed or 
machine-readable form. However, these resources have been treated as
finished and fixed objects and lexicographers themselves have not, in
general, played an active role in theoretical research on the lexicon. Whilst
conventional dictionaries tend naturally to be relatively informal
and unsystematic and, by the very nature
of their organisation, to focus on the individual word rather than
generalisations about classes of lexical items, nevertheless they are
extremely comprehensive by the standards of contemporary theoretical
research. Furthermore, because dictionary making is a commercial activity,
publishing houses have considerable resources and experience of managing
large lexicographical projects and an invaluable storehouse of lexical
knowledge (mostly in the heads of their lexicographers).

The reasons for this comparative lack of collaboration (or at least
rather one-sided and static relationship) stem I think from a perception
on both sides that the other has little to offer. On our side, there has
never been any excuse for this, on that of the publishers, it is more
understandable as they are commercial organisations and printed 
dictionaries represent the product. However, this viewpoint is being
rapidly superseded as electronic publishing for human use becomes a
reality, and the prospect of commercial markets for lexicons incorporable
into NLP systems looms closer. Already there are obvious signs of more
active collaboration, most notably in that centred around the electronic
publication of the {\it Oxford English Dictionary} (Tompa, 1986), but also
in projects such as the British National Corpus (e.g. Summers, 1990).

\subsection{Future Research}

There are many challenging theoretical issues, particularly in the
area of lexical semantics, which must be addressed before anything
like an adequate LKB for most NLP applications can be constructed.
However, the research environment made possible by the availability of
LDBs should make exploration and development of lexical theories
considerably easier --- ideas which until recently had to be tested
introspectively or by laboriously searching through printed
dictionaries can now often be explored in a matter of seconds (see e.g.
papers by Carter and Boguraev \& Briscoe in Boguraev \& Briscoe, 1989). For 
this reason, it is important that LDBs developed within projects such as
ACQUILEX are made widely available within the research community.

The future in computational lexicology and lexicography lies not in
further conversion and exploitation of MRDs, but rather in active
collaboration with lexicographers and dictionary publishers.  Most
dictionary publishers urgently need to make the transition from
separate projects developing printed dictionaries to cumulative and
ongoing development of LDBs. By providing lexicographers with the
tools developed within computational lexicography of the type
described above, the process of dictionary making will become very
much more productive. In addition, at least some lexicographers are
already aware of the opportunities created for novel forms of
dictionary organisation and presentation by the move away from the
printed medium (Atkins, 1991). By making lexicographers aware of the
difficulties and problems encountered in the exploitation of MRDs for
NLP, it should be possible to ensure that the next generation of LDBs
which emerge from publishing houses are far more easily applicable in
NLP (allowing us to move on from the relatively ephemeral issues of 
MRD exploitation.) Most publishers will also need access to computational
linguistic tools to assist their lexicographers with corpus analysis.
Such tools would not replace the lexicographer, but would be used to
provide an initial organisation and selection of corpus data which
should improve both quality and productivity.  Once again NLP
researchers are well placed to provide such tools.

I hope that this rather brief and very selective introduction has
provided the reader not familiar with work on the lexicon or
computational lexicography with a comprehensible overview and enough
references to follow up points of interest. For those working in the
field, I hope that my necessarily sketchy and sometimes contentious
analysis has provoked thought rather than offence.  Quite correctly,
the lexicon is now firmly established on the agenda within
(computational) linguistic theory and NLP. However, in my opinion the
subfield of computational lexicology and lexicography which has burgeoned 
around the
exploitation of MRDs has now served its useful purpose. The future
lies in the more illusive but infinitely more rewarding prospect of
fruitful and genuine collaboration between linguists, lexicographers
and NLP researchers supported and fostered by their respective funding
agencies and companies.

\section*{Acknowledgements}

This work is supported by ESPRIT grant BRA 3030 entitled `The
Acquisition of Lexical Knowledge for Natural Language Processing
Systems' to the Universities of Cambridge, Pisa, and Amsterdam,
University College Dublin and the Universitat Politecnica de
Catalunya, Barcelona.  I am grateful to my colleagues on this project
for their discussion about many of the issues addressed here and, in
particular, to Ann Copestake and Antonio Sanfilippo for their
comments, advice and help.  The opinions expressed, however, are those
of the author alone, as are any remaining errors.


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#3,} #4}

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\end{list}

\end{document}
