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<Paper uid="W91-0201">
  <Title>Knowledge represen tation and knowledge of words*</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
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2. Goals
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    <Paragraph position="0"> *The author acknowledges the support of the National Science Foundation under grant IRI-9003165.</Paragraph>
    <Paragraph position="1"> We need a theory of linguistic meaning that is well grounded in linguistic evidence, that is broad in its coverage of linguistic constructions and explanatory power, that can be integrated with appropriate reasoning procedures, and that provides applicable models for technology, such as machine translation, information retrieval, and word-oriented instructional software. How are we going to achieve these goals? 3. Background in logic My own interest in this topic grew in part out of my work some years ago in Montague grammar. This field has developed into a healthy area of linguistics with many well developed research problems. But fairly early on, it seemed to me that a lot could be learned by concentrating instead on the limitations of the approach; some of these limitations are described in \[Thomason 1987\]. The shortcomings of a logicist approach to semantics are probably clearest in connection with word meaning.</Paragraph>
    <Paragraph position="2"> Knowing such meanings involves access to a broad spectrum of relevant knowledge.</Paragraph>
    <Paragraph position="3"> Technical terms like 'myelofibrosis', make the point most vividly, but (as Minsky and others have often pointed out), it also is true of everyday terms like 'birthday party'.</Paragraph>
    <Paragraph position="4"> A logic-based approach like Montague grammar uses meaning postulates to account for inferences like (1) Bill saw someone kiss Margaret So, someone kissed Margaret In fact, the underlying logic provides a fairly powerful apparatus for writing these postulates. Lambda abstraction over variables of higher-order types enable the postulate writer to attach conditions to words (in the case of this example, to the word 'see') so that the right intersentential consequences will follow. (Roughly, 'see' has the property of expressing a relation such that if anyone is related to a state of affairs by this relation, then that state of affairs obtains. These things look horrible in English, but fine in Intensional Logic.) This condition on 'see', though, is far from a characterization of its meaning; it doesn't distinguish it from a large class of similar terms, such as 'hear', 'learn', 'remember' and 'prove'. And the underlying logic doesn't deliver the capability of providing such characterizations, except in a few cases (like 'and') that are closely connected to the original purpose of the logic: explicating mathematical reasoning.</Paragraph>
    <Paragraph position="5"> Mathematics provides deep chains of exceptionless reasoning, based on relatively few primitives. Thus, concepts can be connected through definitions. Most common sense domains provide relatively shallow patterns of defensible reasoning, based on a large number of loosely connected concepts. It is difficult in many cases to separate what is primitive from what is derived. Given enough background knowledge it is possible to characterize the meanings of terms, but these characterizations seldom take the form of necessary and sufficient conditions. It is difficult to find reliable methods for articulating the background knowledge and general ways of applying such knowledge in characterizing meanings.</Paragraph>
    <Paragraph position="6"> We should remember that similar inadequacies were responsible for the failure of attempts (most notably, by Rudolph Carnap) to extend Frege's formalization of mathematical reasoning to the empirical sciences. 1 Carnap discovered that Frege's method of deriving definitions failed with color terms, and that terms like 'soluble' could not be given I See \[Carnap 36-37\].</Paragraph>
    <Paragraph position="7"> natural and correct definitions in terms of terms like 'dissolve'. The failure of logic-based methods to provide a means of formalizing the relevant background knowledge even in relatively scientific domains provoked a skeptical reaction against the possibility of extending these logical methods. 2 Montague motivated his addition of possible worlds to the Fregean framework with a problem in derivational lexical semantics--that of providing a theory of events that would allow predicates like 'red' to be related to their nominalizations, like 'redness'. s Trying to account for derivational interconnections between word meanings (rather than providing a framework for making principled distinctions in meaning between arbitrary words) is a more modest goal, and much can be learned by extending a logic-based theory in this direction. But the work in lexical semantics that began in \[Dowty 79\] seems again to be limited in fundamental ways by the underlying logic. The definition that Dowty provides of agent causality in terms of event causality fails, for logical reasons, in a way that offers little hope of repairs. And, though the idea of normalcy that Dowty found to be needed in accounting for progressive aspect seems intuitively to sanction defeasible inferences, Intensionai Logic provides no good way of accounting for the validity of examples that have exceptions, like (2) Harry is crossing the street.</Paragraph>
    <Paragraph position="8"> So Harry will cross the street.</Paragraph>
    <Paragraph position="9"> There is a natural progression between examples like this, which are focused on inferential properties of telic constructions, to cases that draw more broadly on world knowledge (in this case, knowledge about the normal uses of artifacts), like  (3) Alice used the match to light a fire.</Paragraph>
    <Paragraph position="10"> So Alice struck the match.</Paragraph>
    <Paragraph position="11"> 4. One relation between lexical semantics and knowledge representation  Linguistic logicist work in the semantics of words, then, is closely related to Iogicist work in knowledge representation. Though the relation has not been much exploited yet, it suggests a clear line of research that is likely to benefit both linguistics and AI. I should add that I am thinking of long-term benefits here. I don't claim that this extension of the logicist inventory will provide a representation scheme for words that is nearly adequate. I do believe that such work is an essential part of any satisfactory solution to the problem of lexical representation. There is research in lexical semantics that is oriented towards applications but lacks a theoretical basis. The logical work, on the other hand, is limited in its applicability to lexical problems but provides an interface with sentence meaning; this approach is at its best in showing how meanings of phrases depend on meanings of their parts. Along with this, it provides a specification of correct reasoning that--though it may not be implementablc is general and precise, and can be essential at the design level in knowledge representation applications.</Paragraph>
    <Paragraph position="12"> Part of the human language capacity is the ability to deal effectively with both words and sentences. Though we may not have a single computational approach that does both, See \[Quine 60\].</Paragraph>
    <Paragraph position="13"> 3See \[Montague 69\].</Paragraph>
    <Paragraph position="14"> we can try to stretch partial approaches towards each other in the hope that together they'll cover what needs to be covered. This is why I am enthusiastic about extensions to the lexical coverage of the logicist approaches.</Paragraph>
    <Paragraph position="15"> Logicist work in AI has generally recognized the need for augmenting the Fregean logical framework in order to deal with problems of common sense reasoning. The most generally accepted line of development is the incorporation of nonmonotonicity into the logic. And this feature, it turns out, is precisely what is needed to accommodate many of the problems that emerged in Montague-style lexical semantics. It is the defeasible nature of telicity, for instance, that makes it difficult to deal with (2) adequately within a standard logical framework. It is no surprise that lexicai semantics is full of defeasible generalizations, and a general technique for expressing such generalizations would greatly extend the coverage of logicist theories of word meaning.</Paragraph>
    <Paragraph position="16"> The available approaches to nonmonotonicity could readily be incorporated into the framework of Montague-style semantics without any changes to the undefeasible part of the logic. 4 Thus, the linguistic side has much to gain from the work in AI.</Paragraph>
    <Paragraph position="17"> Work on common sense reasoning, on the other hand, would also gain much from cooperative applications to the study of derived word meanings. For one thing, the project of accounting for such meanings discloses a limited number of notions that are obviously of strategic importance for common sense reasoning. 5 Moreover, the linguistic work uses a well developed methodology for marshaling evidence and testing theories. Given the difficulty of delineating common sense reasoning and deciding between competing theories, this methodology could be very useful to the AI community.</Paragraph>
    <Paragraph position="18"> On the whole, then, this seems like a very natural and promising partnership.</Paragraph>
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