Intergrating Symbolic and Statistical Representations: The Lexicon Pragmatics Interface

Integrating Symbolic and Statistical Representations: The Lexicon Pragmatics Interface Ann Copestake Center for the Study of Language and Information, Stanford University, Ventura Hall, Stanford, CA 94305, USA aac~csl£, stanford, edu Alex Lascarides Centre for Cognitive Science and Human Communication Research Centre, University of Edinburgh, 2, Buccleuch Place, Edinburgh, EH8 9LW, Scotland, UK alex@cogsci, ed. ac. uk Abstract We describe a formal framework for inter- pretation of words and compounds in a discourse context which integrates a sym- bolic lexicon/grammar, word-sense proba- bilities, and a pragmatic component. The approach is motivated by the need to han- dle productive word use. In this paper, we concentrate on compound nominals. We discuss the inadequacies of approaches which consider compound interpretation as either wholly lexico-grammatical or wholly pragmatic, and provide an alternative inte- grated account. 1 Introduction VVhen words have multiple senses, these may have very different frequencies. For example, the first two senses of the noun diet given in WordNet are: O 1. (a prescribed selection of foods) => fare - (the food and drink that are regularly consumed) 2. => legislature, legislative assembly, general as- sembly, law-makers ]k|ost English speakers will share the intuition that the first sense is much more common than the sec- ond, and that this is (partly) a property of the word and not its denotation, since near-synonyms oc- cur with much greater frequency. Frequency differ- ences are also found between senses of derived forms (including morphological derivation, zero-derivation and compounding). For example, canoe is less fre- quent as a verb than as a noun. and the induced ac- tion use (e.g., they canoed the kids across the lake) is much less frequent than the intransitive form (with location PP) (they canoed across the lake). 1 A de- rived form may become established with one mean- ing, but this does not preclude other uses in suffi- ciently marked contexts (e.g., Bauer's (1983) exam- ple of garbage man with an interpretation analogous to snowman). Because of the difficulty of resolving lexical am- biguity, it is usual in NLP applications to exclude 'rare' senses from the lexicon, and to explicitly list frequent forms, rather than to derive them. But this increases errors due to unexpected vocabulary, espe- cially for highly productive derivational processes. For this and other reasons it is preferable to as- sume some generative devices in the lexicon (Puste- jovsky, 1995). Briscoe and Copestake (1996) argue that a differential estimation of the productivity of derivation processes allows an approximation of the probabilities of previously unseen derived uses. If more probable senses are preferred by the system, the proliferation of senses that results from uncon- strained use of lexical rules or other generative de- vices is effectively controlled. An interacting issue is the granularity of meaning of derived forms. If the lexicon produces a small number of very underspeci- fled senses for a wordform, the ambiguity problem is apparently reduced, but pragmatics may have insuf- ficient information with which to resolve meanings, or may find impossible interpretations. We argue …

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