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\begin{document}
\thispagestyle{empty}
\vspace*{2in}
\begin{center}
{\Large \bf Sense extensions as Lexical Rules}\\
\smallskip
Ted Briscoe and Ann Copestake\\
\smallskip
University of Cambridge Computer Laboratory\\
New Museums Site, Pembroke Street, Cambridge, CB2 3QG, UK\\
\smallskip
June 1991\\
\smallskip
(In Proceedings of
the IJCAI Workshop on
Computational Approaches to Non-Literal Language,
Sydney, Australia)\\
\bigskip
ESPRIT BRA-3030 ACQUILEX Working Paper No. 022
\end{center}
\newpage
\setcounter{page}{1}
\begin{center}
{\large \bf Sense extensions as Lexical Rules}
\\
\bigskip
Ted Briscoe and Ann Copestake\\
\medskip
Computer Laboratory, University of Cambridge, Pembroke Street,
Cambridge, CB2 3QG, UK\\
ejb@cl.cam.ac.uk aac@cl.cam.ac.uk
\end{center}
\begin{abstract}
We define a declarative and computationally tractable concept of
lexical rule as a component of a unification-based lexicon employing
(default) inheritance and typed feature structures. We argue that this
concept of a lexical rule is expressive enough to cover both derivational
morphological processes and metonymic and metaphoric sense extensions,
and that the application of such rules is productive within finely
specified subsets of the lexicon identifiable via the type system. In
addition, we argue that formulation of these processes in a uniform
framework uncovers similarities between them; such as the blocking of
metonymic sense extension processes in a fashion similar to the
blocking of derivational morphological ones. We illustrate these
points principally with reference to a family of increasingly specific
rules of ``grinding'' and indicate how machine-readable versions of
conventional dictionaries can be utilised as a source of information
to guide identification and specification of such rules.
\end{abstract}
\section{Introduction}

In this paper, we take the position, persuasively argued in
Pustejovsky (1989a,b), that the lexicon is not a passive list of
unrelated entries, but rather a highly structured and generative
device. The rule-governed processes which can be expressed as lexical
rules extend far beyond those discussed under the rubric of 
lexical redundancy rule in classical generative grammar. We present a
very general, though declarative and computationally tractable,
definition of a lexical rule, formalised within the framework of
unification-based approaches to lexical organisation employing typed
feature structures and (default) inheritance. We are currently
exploring the possibility that this account of lexical rules is
sufficiently expressive to describe processes of derivational
morphology, conversion or zero-derivation, and metonymic as well as
metaphoric sense extension. Linguistic research has emphasised the
phonological / graphological consequences of derivational processes to
the extent that conversion or zero-derivation has often been treated
in terms of ``zero-morphemes'' and ``zero-affixation''. In our work,
we are concentrating on the syntactic and semantic consequences of
such processes and treat all of them as mappings between lexical
entries expressed as typed feature structures. In this framework, it
is affixation, rather than conversion, which is the marked operation
since it requires changes to the phonological or orthographic part of
the lexical entry (see, e.g., Cahill (1990) for a theory of such
operations couched in a unification-based account of the lexicon). We
argue that there are sufficient similarities between these processes
to treat them as lexical rules and that the distinction between
metonymic processes of sense extension and more clearly derivational
rules is not as clear-cut as it is sometimes assumed.

A standard example of metonymic sense extension is the use of a word
denoting a place to refer to (some of) the people inhabiting that
place (e.g. ``village'', ``palace''). This process seems to involve
the foregrounding of one component of the meaning of the place
denoting word -- we follow Pustejovsky (1989a,b,c) in assuming that the
lexical semantic representation of nouns is richer than is standardly
assumed in generative linguistic theory and that, in particular, the
``qualia structure'' for such nouns will contain (telic) information
which allows direct access to the information that they are
inhabited. A well-known example of metaphoric sense extension is that
involving use of a word denoting an animal to refer to humans (``John
is a pig'', ``John is a wombat'' etc). Although the sense extension
from animals into metaphorical senses denoting humans with some
particular characteristic is apparently productive, the actual
characteristics involved, and even whether the word can be applied to
men or to women or both, cannot be predicted from knowledge of the
animal sense. Thus, the properties ascribed to a person by ``pig'' are
arguably no more than stereotypical associations with the animal,
rather than central aspects of its meaning / qualia structure. In the
case of ``wombat'' we would argue that the association of foolishness
derives from the phonological form of the word, rather than beliefs
about the animal. Despite the more associative or analogical nature of
metaphorical sense extension, we would argue that there is a core
component to such processes which should be expressed in terms of a
lexical rule, rather than in terms of general purpose reasoning. As
with the metonymic cases (Pustejovsky, 1989c; Briscoe et al., 1990),
we believe that the notion of coercion during syntactic and semantic
interpretation provides an account of when a metaphorical
interpretation will be adopted, and we would like to characterise the
limits of coercion in terms of possible mappings defined by lexical
rules.

An example of a derivational morphological process is the addition of
the ``er'' suffix to verbs, typically creating a noun denoting the
agent of the action denoted by the verb (e.g. ``teach'', ``teacher'').
There are several apparent differences between this type of process
and the metonymic and metaphoric sense extensions considered above.
The derivational rule involves a change of syntactic class, it affects
the argument structure of the derived predicate, it involves
affixation, and although there is a foregrounding of one aspect of the
verb meaning, the result would not traditionally be described as a
metonymic, or indeed metaphorical, usage. Nevertheless, there are
clearly derivational processes which do not affect syntactic class
(e.g. ``re-program'', ``un-reprogrammable'') and sense extensions
which do; for example, countability of nouns changes depending on
whether they are interpreted as types, substances or portions (e.g.
``There was beer all over the table'', ``John drank a beer''). Not all
derivational processes affect argument structure (e.g. ``un-kind''),
whilst metonymic sense extensions (e.g. ``John enjoyed the film'',
John finished the beer'') can; at least when given the analysis of
Pustejovsky (1989c) and Briscoe et al. (1990). Finally, processes of
conversion and derivation can be identical; for example, both
``purchase'' and ``replace'' have deverbal nominal forms ``purchase''
and ``replacement'', both nouns can denote the action involved and
take appropriate complements (``Bill's purchase of his new car'',
``Bill's replacement of Sue with Mary''), and both can denote the
result of the action (``Bill's purchases were many and varied''
``Bill's replacement was Sue''). Traditionally, this latter
resultative meaning would be described as metonymic and probably
specialised and non-productive. We think that our definition of
lexical rule will allow an account of both as productive syntactic and
semantic operations mapping between lexical entries. The difference
between metaphorical and metonymic operations is a matter of the
degree to which the lexical rule determines or circumscribes the
eventual interpretation.

There are other similarities between sense extension and derivational
morphology; clearly, productivity is an issue in both, and in
particular, sense extension processes may apparently be blocked
(preempted by synonymy), in a way comparable to the situation in
derivational morphology (see e.g. Bauer 1983:87f). For example, the
regular form ``stealer'' does not generally occur, apparently because
of the availability of ``thief''. Another productive metonymic sense
extension is that of animal denoting (count) nouns to (mass) nouns
denoting their meat (e.g. ``lamb''), but this process too is blocked
by the presence of a synonymous lexeme with different form (``pig'',
``pork''). By representing such processes in terms of lexical rules
mapping between entries, we hope to account for blocking in terms of
syntactic and semantic identity with an entry defined without recourse
to the relevant lexical rule. In addition, we hope to express sense
extension processes, and indeed derivational ones, as fully productive
processes which apply to finely specified subsets of the lexicon,
defined in terms of both syntactic and semantic properties expressed
in the type system.

As part of the ACQUILEX project\footnote{ `The Acquisition of lexical
knowledge for Natural Language Processing systems' (Esprit BRA-3030)}
we are investigating the occurrence of such extended and derived senses in
machine readable dictionaries (MRDs).  Although many regular
relationships are recognised by lexicographers 
(Ostler and Atkins 1991), the
representation of these extended and derived senses in existing MRDs
is unsystematic.  Thus our lexical knowledge base has to be able to
deal with extensions which are never given a separate sense (``John is a
wombat''), even if it is not possible to represent them completely.
In this paper, we first examine in more detail some of the classes of
sense extensions and their realisation in {\it The Longman Dictionary
of Contemporary English} (LDOCE).  We then describe the representation
language of the lexical knowledge base (LKB) which is being used to
represent information extracted from MRDs on the ACQUILEX project, and
illustrate how it is possible to represent two of the examples
mentioned.  The LKB is unification-based; it supports a restricted
range of operations; (default) unification, (default) inheritance and
lexical rule application and is intended as a lexical rather than a
general purpose knowledge representation.  However, we are using it to
represent a relatively rich range of semantic information which allows
us to indicate some of the shifts in meaning exemplified both in sense
extension and in derivational morphology and which we hope will allow
us to investigate the examples where productive processes are blocked.

\section{The representation of sense extensions in dictionaries}

We will consider two types of sense extension, both of
which apply to words which denote animals.
The first is the metaphorical use, mentioned earlier.
The second involves the extension to a non-count
sense with a meaning which can be paraphrased
as ``some substance derived from that animal'', where this will normally be
either fur/skin or meat/flesh.
We
in fact think of this as a special case of a more general sense extension rule
which we will refer to as ``grinding''.
It is well known that any count noun denoting a physical object can be
used in a mass sense to denote a substance derived from that object,
when it occurs
in a sufficiently marked context.  We refer to
this as `grinding' because the context normally suggested is the
``Universal Grinder'' (see Pelletier and Schubert 1986). 
So if ``a table'' is ground up the result can be
referred to as ``table'' (``there was table all over the floor'').
Several regular sense extensions 
can be regarded as special cases of `grinding', where the extension
may have become lexicalised.
Thus besides the animal/meat examples,
trees used for wood (``beech'') have a 
sense denoting the wood, and so forth (see Copestake and Briscoe (1991)
for further discussion).

We are investigating the representation of these sense extensions in
LDOCE.
Both extensions appear productive, at least in a sufficiently
marked context; for example in the LOB corpus we find the use of
``mole'' as a mass term:
\begin{quote} Badger hams are a
delicacy in China while mole is eaten in many parts of Africa.
\end{quote}
but even when the use seems conventional it may or may not be
represented as a separate sense
in LDOCE (``lamb'' is given a meat/flesh sense but
``haddock'' is not).
Since space for definitions is very restricted in printed dictionaries,
senses may be omitted even when the
animal is conventionally used for food (the position is also confused
by lexicographers' use of various techniques such as bracketing to
``collapse'' two senses into one).
Furthermore, words 
which primarily denote the meat (eg ``pork'') 
normally block the sense extension process. Nevertheless this can
often still apply in a more ``marked'' fashion.
For example using ``pig'' instead of ``pork'' is possible
but not lexicalised, and its use is marked, perhaps suggesting that
the meat is of very inferior quality.

One technique which we are developing to investigate the extent to
which regular sense extensions can account for polysemy in dictionary
definitions, is to examine exhaustively all cases where a word has a
primary sense in one semantic class (such as animal) and a secondary
sense in another (such as person or substance).  We can achieve this
by deriving relatively complete sense-disambiguated taxonomies from
the dictionary (using techniques described in Copestake (1990)),
starting from the primary
senses of words such as ``person'' and ``animal'' and intersecting the
resulting taxonomies.  Thus in the animal/person case there are over
100 words with senses in both taxonomies, the majority of which appear
to be metaphorical uses.  The other major class involves
nominalisations such as ``scavenger''
being given separate senses (in this case one denoting a creature and
the other a person).  Furthermore the majority of the polysemy, at
least for the class of words denoting animals, appears to be regular. 

\section{Representation in the LKB}

The LKB is designed to represent any information which may be
extracted from MRDs as part of the ACQUILEX project, including both
syntactic and relatively complex semantic information.
The LKB's representation language is based on
the use of typed feature structures similar to those
described in Carpenter (1990). 
Feature structures must be well-formed with respect to types and 
particular features will only be appropriate to specified types and
their subtypes.  
Types
are hierarchically ordered; 
the association of {\em constraints} with types allows
non-default inheritance. 
We augment this
with a restricted concept of default inheritance (allowing only
`orthogonal' multiple inheritance (Touretzky 1986)); 
default inheritance is formalised in terms of 
default unification of feature structures ordered by an inheritance
hierarchy.
The type system constrains both 
default inheritance and lexical rule application.
The LKB's representation language is described in detail in Copestake
et al (1991); the following sections are an informal description
illustrated with relevant examples.

\subsection{The type system}

The type hierarchy defines a partial ordering (notated $\sqsubseteq$)
on the types and specifies
which types are {\em consistent}.  Only feature structures 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.
Every {\em consistent} set of types  $S\subseteq {\sf TYPE}$
has a unique greatest lower bound or
meet (notation  $\sqcap S$).  
This condition allows feature structures
to be typed deterministically --- if two feature structures of types
{\bf a}
and {\bf b} are unified the type of the result will be 
${\bf a} \sqcap {\bf b} $,
which must
be unique if it exists.  If ${\bf a} \sqcap {\bf b}$ 
does not exist unification fails.  Thus in the fragment of a type
hierarchy shown in Figure~1 {\bf artifact} and {\bf physobj} are
consistent with ${\bf artifact} \sqcap {\bf physobj} = {\bf artifact\_obj}$.
\setlength{\unitlength}{0.8in}
\begin{figure}
\begin{center}
\begin{picture}(6, 2.6)
\put(4,2.5){\makebox(0,0){nomrqs}}
\put(4,2.4){\line(-2, -1){0.7}}
\put(4,2.4){\line(2, -1){0.7}}
\put(5,2){\makebox(0,0){artifact}}
\put(5,1.9){\line(-2, -1){0.7}}
\put(3,2){\makebox(0,0){physobj}}
\put(3,1.9){\line(-2, -1){0.7}}
\put(3,1.9){\line(0, -1){0.7}}
\put(3,1.9){\line(2, -1){0.7}}
\put(4,1.5){\makebox(0,0){artifact\_obj}}
\put(4,1.4){\line(0, -1){0.3}}
\put(2,1.5){\makebox(0,0){ind\_obj}}
\put(2,1.4){\line(-2, -1){0.7}}
\put(2,1.4){\line(0, -1){0.3}}
\put(1,1){\makebox(0,0){creature}}
\put(1,0.9){\line(2, -1){0.7}}
\put(1,0.9){\line(-2, -1){0.7}}
\put(0,0.5){\makebox(0,0){person}}
\put(2,0.5){\makebox(0,0){animal}}
\put(2,1){\makebox(0,0){inan\_obj}}
\put(3,1){\makebox(0,0){substance}}
\put(3,0.9){\line(2, -1){0.7}}
\put(4,1){\makebox(0,0){food}}
\put(4,0.9){\line(0, -1){0.3}}
\put(4,0.5){\makebox(0,0){food\_substance}}
% \put(4,0.4){\line(0, -1){0.3}}
% \put(4,0){\makebox(0,0)[b]{meat}}
\end{picture}
\end{center}
\end{figure}

Our system differs somewhat from that described by Carpenter (1990)
in that we adopt a
different notion of well-formedness of typed feature structures. 
In our system
every type must have exactly one associated feature structure which
acts as a constraint on all feature structures of that type;
it subsumes all well-formed feature structures of that type.
The constraint also defines which features are 
{\em appropriate} for a particular type; a well formed feature
structure 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 feature structure of that type; 
all constraints must therefore be mutually consistent.
Constraints can be seen as extending the PATR-II notion
of templates in that the inheritance of constraints 
allows concise definitions of all feature
structures, not just lexical entries; however the type system also
prevents inappropriate features occurring.

For example the constraints associated with the
types {\bf artifact} and {\bf physobj} might be:
\begin{quote}
{\scriptsize
   $\avmplus{\att{artifact}\\
                  \attvaltyp{purpose}{formula}}$}\\
{\scriptsize
   $\avmplus{\att{physobj}\\
                  \attvaltyp{form}{physform}}$}
\end{quote}
Bold case indicates types; thus, for instance the ``value'' {\bf formula}
can be any feature structure of this type.
The constraint on {\bf
artifact\_obj} will contain information inherited from both parents,
thus:
\begin{quote}
{\scriptsize
   $\avmplus{\att{artifact\_obj}\\
                  \attvaltyp{form}{physform}\\
                  \attvaltyp{purpose}{formula}}$}
\end{quote}
Further examples of constraints and features which we will use in this
paper are:
\begin{quote}
{\scriptsize
   $\avmplus{\att{ind\_obj}\\
               \attval{form}{\avmplus{\att{physform}\\
               \attvaltyp{shape}{individuated}}}}$}\\
{\scriptsize
   $\avmplus{\att{creature}\\
                  \attvaltyp{age}{scalar}\\
                  \attvaltyp{sex}{gender}}$}\\
{\scriptsize
   $\avmplus{\att{animal}\\
                  \attvaltyp{edible}{boolean}}$}\\
{\scriptsize
   $\avmplus{\att{substance}\\
               \attval{form}{\avmplus{\att{physform}\\
               \attvaltyp{shape}{unindividuated}}}}$}\\
{\scriptsize
   $\avmplus{\att{food}\\
                  \attval{purpose}{\avmplus{\att{formula}\\
                          \attvaltyp{pred}{eat}}}}$}
\end{quote}
All these types are intended to represent that part of the lexical
entry which contains relatively complex semantic information, the
``relativised qualia structure'' (Calzolari 1991).

We shall also make use of the following types to define syntactic
properties etc:
\begin{quote}
{\bf lex-sign} $\sqsubseteq $ {\bf top} \hspace{0.1in}
{\scriptsize
   $\avmplus{\att{lex-sign}\\
             \attvaltyp{orth}{string}}$}\\
{\bf noun} $\sqsubseteq $ {\bf lex-sign} \hspace{0.1in}
{\scriptsize
   $\avmplus{\att{noun}\\
             \attval{syntax}{\avmplus{\attvaltyp{count}{boolean}}}\\
             \attvaltyp{rqs}{nomrqs}}$}\\
{\bf count-noun} $\sqsubseteq $ {\bf lex-sign} \hspace{0.1in}
{\scriptsize
   $\avmplus{\att{noun}\\
             \attval{syntax}{\avmplus{\attvaltyp{count}{$+$}}}}$}\\
{\bf mass-noun} $\sqsubseteq $ {\bf lex-sign} \hspace{0.1in}
{\scriptsize
   $\avmplus{\att{noun}\\
             \attval{syntax}{\avmplus{\attvaltyp{count}{$-$}}}}$}
\end{quote}

The feature structure below is
well-formed since it
contains all the 
appropriate features and no inappropriate ones,
it is subsumed by the constraints on
its type and all its substructures are well-formed.
\begin{quote}
{\scriptsize
   $\avmplus{\att{count-noun}\\
             \attvaltyp{orth}{``haddock''}\\
             \attval{syntax}{\avmplus{\attvaltyp{count}{$+$}}}\\
             \attval{rqs}{\avmplus{\att{animal}\\
                              \attvaltyp{sex}{gender}\\
                              \attvaltyp{age}{scalar}\\
                              \attvaltyp{edible}{boolean}\\
                              \attval{form}{\avmplus{\att{physform}\\
                                  \attvaltyp{shape}{individuated}}}}}}$}
\end{quote}

Given the type system introduced above, a lexical entry in the LKB:
\begin{verbatim}
haddock 1 count-noun
       <rqs> = animal.
\end{verbatim}
would be expanded out into such a feature structure\footnote{The
actual type system being employed is considerably more complex, since
only the relevant features are being shown in these examples.}.

\subsection{Lexical rules}

A lexical rule in the LKB is a feature structure of type {\bf lexical-rule}.  
The expanded constraint for the type is:
\begin{quote}
{\scriptsize
   $\avmplus{\att{lexical\_rule}\\
             \attvaltyp{0}{lex\_sign}\\
             \attvaltyp{1}{lex\_sign}}$} 
\end{quote}
thus all lexical rules have to have the features 0 and 1 which must
both have values which are of type {\bf lex\_sign}.  

New lexical signs may be generated by unifying a copy of
the lexical entry with the feature structure at the end of the path 
\verb|<1>|
in a copy of the lexical rule --- the feature structure at the end of
the path \verb|<0>| is then the new lexical sign.  Lexical rules are indexed by
the type of their ``input'' and ``output'' feature 
structures, so they will only be applied to entries
of the appropriate type.

A general type for grinding lexical rules
could be specified in the LKB as follows:
\begin{quote}
{\bf grinding} $\sqsubseteq$ {\bf lexical\_rule} \hspace{0.1in}
{\scriptsize
   $\avmplus{\att{grinding}\\
             \attval{1}{\avmplus{\att{count-noun}\\
                         \attval{orth\ \ \ }{\ind{1}}\\
                         \attvaltyp{rqs}{ind\_obj}}}\\
             \attval{0}{\avmplus{\att{mass-noun}\\
                         \attval{orth\ \ \ }{\ind{1}}\\ 
                         \attvaltyp{rqs}{substance}}}}$}
\end{quote}
The effect of the lexical rule is to transform a count noun with the
``relativised qualia structure'' (RQS) properties appropriate to 
an individuated physical object {\bf ind\_obj} into a mass noun
with properties appropriate for a substance {\bf substance}.

We specialise the grinding rule to allow for cases 
such as the animal/meat extension explicitly.
The typed framework provides us with a natural
method of characterising the subparts of the lexicon to which such
rules should apply.
The lexical rules can, in effect, be parameterised by inheritance in 
the type system.  
For example given the type hierarchy shown in Figure~1 we can then give 
rules which inherit information from {\bf grinding} 
such as animal\_grinding: 
\begin{quote}
animal\_grinding \hspace{0.1in}
{\scriptsize
   $\avmplus{\att{grinding}\\
             \attval{1}{\avmplus{\attval{rqs}{\avmplus{\att{animal}\\
                                  \attvaltyp{edible}{+}}}}}\\
             \attval{0}{\avmplus{\attvaltyp{rqs}{food\_substance}}}}$}
\end{quote}
Thus given the lexical entry for ``haddock'' shown above
we can apply the lexical rule to generate a sense meaning
``haddock-flesh''
(partially represented as):
\begin{quote}
{\scriptsize
   $\avmplus{\att{mass-noun}\\
             \attvaltyp{orth}{haddock}\\
             \attval{syntax}{\avmplus{\attvaltyp{count}{$-$}}}\\
             \attval{rqs}{\avmplus{\att{food\_substance}\\
                              \attval{purpose}{\avmplus{\att{formula}\\
                                   \attvaltyp{pred}{eat}}}}}}$}
\end{quote}
(where the specification of the value {\bf eat} for the purpose role
arises from the constraint on the type {\bf food\_substance}, 
inherited from {\bf food},
and the type mass-noun arises from the grinding type.)

The lexical rule for the metaphorical use of animal words might be
something like:
\begin{quote}
animal\_metaphor \hspace{0.1in}
{\scriptsize
   $\avmplus{\att{lexical\_rule}\\
             \attval{1}{\avmplus{\attval{syntax\ \ \ }{\ind{2}}\\
                                 \attval{orth\ \ \ }{\ind{3}}\\
                                 \attval{rqs}{\avmplus{\att{animal}\\
                                  \attval{sex\ \ \ }{\ind{1}}}}}}\\
             \attval{0}{\avmplus{\attval{syntax\ \ \ }{\ind{2}}\\
                                 \attval{orth\ \ \ }{\ind{3}}\\
                                  \attval{rqs}{\avmplus{\att{person}\\
                                  \attval{sex\ \ \ }{\ind{1}}}}}}}$}
\end{quote}


In principle, multiple lexical rules may be applied in sequence.  For
example another lexical rule which we might specify is ``portioning''
which would convert food (or drink) denoting mass nouns into count
nouns denoting a portion of that substance (eg ``three beers'', and
even ``three haddocks'' in the context of a restaurant).  All outputs
of lexical rules must be potentially valid lexical entries.  In the
case of zero-derivational processes, like those we have been defining,
we wish to restrict the set of lexical rules so that application may
not be circular --- that is if there is a lexical rule which could
generate the set of feature structures F2 from the set F1, no other
lexical rule or sequence of lexical rules may be specified which could
generate any member of F1, or a feature structure subsuming any member
of F1, starting from any member of the set F2, since lexical rule
application would not then terminate.  One straightforward way of
ensuring this condition is met is to constrain the set of lexical
rules so that if a rule which transforms feature structures of type
{\bf t\_1} to {\bf t\_2} exists no sequence of rules exists which
transforms {\bf t\_2} to {\bf t\_1} or any supertype of {\bf t\_1}.
However, this condition is overrestrictive because some types of
derivational rule can apply to their own output iteratively
(``meta-meta-theory'', ``anti-anti-missile'',
``great-great-grandmother'', ``re-re-program''). Such differing
properties of lexical rules can be formalised as they emerge by
creating distinct types of lexical rule and placing well-formedness
constraints on each type. Expressing constraints of this type will
require a more expressive language than we currently deploy in the
LKB, in the spirit of that described in Carpenter 
et al. (1991). The richer these
constraints the more a substantive theory of lexical processes will
emerge within the formal computational framework outlined above.

\subsection{Default inheritance}

To allow default inheritance we introduce the concept of {\em psort};
a feature structure from which another feature structure inherits
information, by default.  The hierarchical ordering on psorts (which
must be consistent with the type hierarchy) provides an order on
defaults.  Default inheritance is implemented by a version of default
unification.  Only orthogonal multiple inheritance (Touretzky 1986) is
allowed; information inherited from multiple parents must not be
contradictory.  A default inheritance hierarchy which connects
semantic parts of lexical entries can be derived semi-automatically
from taxonomies extracted from MRDs (Copestake 1990). Defaults may
also be useful in the representation of syntactic information in the
lexicon (e.g. Flickinger, 1987)

The use of default inheritance in which we are primarily interested
here is to allow more specific information about an extended word
sense extracted from an MRD to augment and possibly override
information inherited from the result of application of a lexical
rule.  In cases where the lexical rule predicts the extended sense
correctly, the specific information will duplicate information already
present.  If the rule is correct, but incomplete, the specific
information will augment the inherited information. If it is partially
incorrect, the more specific information will override that inherited
from the result of lexical rule application.

For example, if the result of applying the lexical rule to a sense is
notated as sense+rule-name (eg lamb\_1+animal\_grinding) then the
representation of the LDOCE sense lamb (2) (the meat)
in the LKB might be:
\begin{verbatim}
lamb 2 < lamb_1+animal_grinding.
\end{verbatim}
where \verb|<| is used to indicate default inheritance from a psort.
In this case
no extra information need be added.  In contrast the entry for
lamb (3) (``a young gentle person'') might augment the information inherited
from the lexical rule:
\begin{verbatim}
lamb 3 < lamb_1+animal_metaphor
< rqs : age > = low.
\end{verbatim}
In the case of haddock, where no LDOCE entry is found, the structure
derived from the lexical rule alone would be used.

Both the types of sense extension that we have been considering seem
to be productive.  Examples such as: 
\begin{quote}
Don't be such a wombat!
\end{quote}
(said to someone behaving stupidly, in the speaker's opinion) seem to
be perfectly acceptable, but such a use of ``wombat'' is probably not
found in any dictionary.  If we are to attempt to process such
utterances, the structure generated by the lexical rule itself has to
be sufficient.  This can give the information that ``wombat'' may
denote a person, but not what the characteristics imputed are, since
these are not predictable.  However, most such uses occur in marked
contexts where it is relatively obvious (to a human reader) what
characteristics are meant (and in any case information from the
lexical rule alone would be sufficient for many NLP applications).
Such lexical rules should only be invoked in a suitably marked context
and we have argued elsewhere, on the basis of corpus data, that coercion
is triggered by predicational environment and subsequent overriding
of the default metonymic interpretation (or perhaps elaboration of the
metaphorical one) is triggered by marked, informationally-rich contexts
(see Briscoe et al., 1990; Copestake and Briscoe, 1991). Below we give
an example of metaphorical use of ``shark'' taken from the SEC corpus
which illustrates these (and some other) processes. (Note that the
sense of ``shark'' involved here is not related to the arguably
distinct homograph relating to (financial) malpractice or criminality.)
\begin{quote}
Nowhere more so than on the world's golf courses, where a man they
call ``the great white shark'' was on the prowl... That one little tap
in at Turnberry put paid... to the cruel jibe that Greg Norman was
just a great white fishfinger. His transformation to a genuine killer
shark began on that windswept course...
\end{quote}

\section{Conclusion}

We have illustrated how some cases of metonymic and
metaphorical use of words might be at least partially
represented in a lexical knowledge
base system, with much the same apparatus as is needed for the
representation of derivational morphology.
Our work on using MRDs to investigate this behaviour is at an early
stage; we have illustrated how relatively crude techniques like
taxonomy intersection can be used to get at some of the classes
of words which undergo this behaviour, but in order to do this more
completely we need to use more sophisticated extraction techniques.
The (partial) representation of information derived from MRDs
in the LKB should give us a way to investigate such sense extensions
as well as being practically useful in NLP systems.

It is at least arguable that much of the information that we are
representing here is real world knowledge rather than lexical
information, and it is clear that our representation is only partial;
both because
we are simplifying information from the definitions and forcing it
into limited templates provided by the type system and because
the LKB's limited operations fall far short of an inference system that
is capable of reasoning about real world knowledge.  We have argued
elsewhere (Briscoe et al (1990), 
following Pustejovsky (1989)) that lexical processes do involve
far more semantic information than is generally represented in the
lexicons of NLP systems, but a further practical argument for
attempting this sort of representation is that it is needed to detect
when lexical rule application may be ``blocked'' by the existence of
other word forms.

As mentioned above the phenomenon of blocking
appears to occur with some cases of regular sense extension in a way
that seems similar to derivational morphology.
In the cases where a word sense exists such as
``pork'' we need to recognise its equivalence with the sense obtained
from ``pig'' using the lexical rule.
To do this we need a rich representation which indicates information
such as ``origin'' which has not been mentioned above.
Because words like
``pig'' can be used in the extended sense, in marked contexts,
the correct way to represent blocking in the LKB is not to prevent application
of the lexical rule but the generated occurrence should be noted as 
being in some way
peculiar. 
Sense extensions may also be blocked if a phonologically similar word
exists which could be confused with the extended sense; for example it
appears unusual for ``deer'' to be used in a metaphorical
sense presumably because it could be confused with ``dear'';
again this phenomenon apparently also exists in derivational
morphology (Bauer 1983:97). 
However these effects are not well-understood --- 
for example blocking
does not seem to apply to the metaphorical sense extensions in the
same way (for example {\bf colt (2)}, {\bf fledgling (2)} and 
{\bf greenhorn (1)} are given very similar definitions in LDOCE).
Rather than attempting to decide on a representation at this point we
intend to investigate the effect on some comprehensive set of
examples, using the MRDs, to try and establish a suitable treatment.

\section*{References}
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#3,} #4}

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\end{document}



