IRCS Technical Reports Series

Document Type

Thesis or dissertation

Date of this Version

12-1-1993

Comments

University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-93-42.

Abstract

Selectional constraints are limitations on the applicability of predicates to arguments. For example, the statement “The number two is blue” may be syntactically well formed, but at some level it is anomalous — BLUE is not a predicate that can be applied to numbers.

According to the influential theory of (Katz and Fodor, 1964), a predicate associates a set of defining features with each argument, expressed within a restricted semantic vocabulary. Despite the persistence of this theory, however, there is widespread agreement about its empirical shortcomings (McCawley, 1968; Fodor, 1977). As an alternative, some critics of the Katz-Fodor theory (e.g. (Johnson-Laird, 1983)) have abandoned the treatment of selectional constraints as semantic, instead treating them as indistinguishable from inferences made on the basis of factual knowledge. This provides a better match for the empirical phenomena, but it opens up a different problem: if selectional constraints are the same as inferences in general, then accounting for them will require a much more complete understanding of knowledge representation and inference than we have at present.

The problem, then, is this: how can a theory of selectional constraints be elaborated without first having either an empirically adequate theory of defining features or a comprehensive theory of inference?

In this dissertation, I suggest that an answer to this question lies in the representation of conceptual knowledge. Following Miller (1990b), I adopt a “differential” approach to conceptual representation, in which a conceptual taxonomy is defined in terms of inferential relationships rather than definitional features. Crucially, however, the inferences underlying the stored knowledge are not made explicit. My hypothesis is that a theory of selectional constraints need make reference only to knowledge stored in such a taxonomy, without ever referring overtly to inferential processes. I propose such a theory, formalizing selectional relationships in probabilistic terms: the selectional behavior of a predicate is modeled as its distributional effect on the conceptual classes of its arguments. This is expressed using the information-theoretic measure of relative entropy (Kullback and Leibler, 1951), which leads to an illuminating interpretation of what selectional constraints are: the strength of a predicate’s selection for an argument is identified with the quantity of information it carries about that argument.

In addition to arguing that the model is empirically adequate, I explore its application to two problems. The first concerns a linguistic question: why some transitive verbs permit implicit direct objects (“John ate Ø”) and others do not (“*John brought Ø”). It has often been observed informally that the omission of objects is connected to the ease with which the object can be inferred. I have made this observation more formal by positing a relationship between selectional constraints and inferability. This predicts (i) that verbs permitting implicit objects select more strongly for (i.e. carry more information about) that argument than verbs that do not, and (ii) that strength of selection is a predictor of how often verbs omit their objects in naturally occurring utterances. Computational experiments confirm these predictions.

Second, I have explored the practical applications of the model in resolving syntactic ambiguity. A number of authors have recently begun investigating the use of corpus-based lexical statistics in automatic parsing; the results of computational experiments using the present model suggest that many lexical relationships are better viewed in terms of underlying conceptual relationships. Thus the information-theoretic measures proposed here can serve not only as components in a theory of selectional constraints, but also as tools for practical natural language processing.

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Date Posted: 20 September 2006