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2013 | OriginalPaper | Buchkapitel

2. Type Theory and Lexical Decomposition

verfasst von : James Pustejovsky

Erschienen in: Advances in Generative Lexicon Theory

Verlag: Springer Netherlands

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Abstract

In this chapter, I explore the relation between methods of lexical representation involving decomposition and the theory of types as used in linguistics and programming semantics. I identify two major approaches to lexical decomposition in grammar, what I call parametric and predicative strategies. I demonstrate how expressions formed with one technique can be translated into expressions of the other. I then discuss argument selection within a type theoretic approach to semantics, and show how the predicative approach to decomposition can be modeled within a type theory with richer selectional mechanisms. In particular, I show how classic Generative Lexicon representations and operations can be viewed in terms of types and selection.

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Vorheriges Kapitel Introduction

Nächstes Kapitel A Type Composition Logic for Generative Lexicon

The admission that mentalese appears to be a first order language is already an acceptance that some sort of decomposition is desirable or necessary for describing language. But beyond this, we will see that the vocabulary accepted as standard to discuss verb behavior is a further commitment to types or categories as part of lexical descriptions.

Each of these strategies has been thoroughly explored in the literature. What I hope to illustrate here is the organization of these approaches according to the above classification. The focus in the discussion below will be on verbs and their projection to syntactic form.

This is the \( \theta \)-theory in varieties of Chomsky’s framework from the 1980s, and the Functional Uniqueness Principle from LFG.

For the present discussion, I assume that the subpredicates in the expressions below are related by means of standard first order logical connectives.

The neo-Davidsonian position adopted by Kratzer (1994) does not fall into this category, but rather in the supralexical decomposition category below. Reasons for this will become clear in the discussion that follows.

Whether the concept of married is any less complex than that of the definiendum bachelor has, of course, been a matter of some dispute. Cf. Weinreich (1972).

Recall that such collapsing operations were an important process prior to lexical insertion in Generative Semantics, cf. McCawley 1972; Dowty 1979.

In programming languages, the operation of semantic analysis verifies that the typing assignments associated with expressions are valid. This is essentially done in compilation time, as a pre-test, filtering out arguments that would otherwise have the wrong type. In a model that does not perform predicate decomposition to incorporate typing constraints, sentences like (19b) are just false.

Regarding argument selection, there are two possible strategies for how the argument accommodates to the typing requirement. Given that the type requirement is a pretest, the argument expression can fail (strict monomorphic typing), or coerce to the appropriate type (polymorphic typing). We will not discuss coercion in the context of the fail early strategy in this paper.

This brings up the issue of how a pre-test is related to the presuppositional interpretation of argument selection. Although an important question, I will defer discussion to a forthcoming treatment of selection mechanisms, Pustejovsky (forthcoming).

This class of adjectives has been studied extensively. Bouillon (1997) analyzes such constructions as subselective predication of a qualia role in the head. Larson and Cho (2003) provide a more conventional interpretation without the need for decompositional representations.

In both (40b) and (40c), interpretations are possible with modification over the object, but they are semantically marked with bright and contradictory with long.

It is worth noting that the propositions formed by the composition of a natural predicate with natural type entities have a special status, since they form the basis of what we will call natural propositions. Examples of such propositions are given below:

The rabbit died.
The rock touches the water.
The ants are under the tree.

It is interesting to compare this to Anscombe’s (1958) discussion and Searle’s (1995) extension regarding “brute facts” as opposed to “institutional facts.”. The natural predication of a property over a natural entity is a judgment requiring no institutional context or background. Facts (or at least judgments) can be classified according to the kinds of participant they contain; in fact, as we shall see, the qualia and the principle of type ordering will allow us to enrich this “fact classification” even further.

Dipert makes a similar move in his 1993 book Artifacts, Art Works, and Agency.

The judgments expressed by the predication of an artifactual predicate of an artifactual subject results in an artifactual proposition. This is formally similar to Searle’s notion of institutional fact.

It might be possible to view pure selection as incorporating the accommodation rule as well, which would result in a more symmetric distribution of behavior in the table. Whether this is computationally desirable, however, is still unclear.

Exploitation on the info element of the dot object for book occurs in examples such as

(i)	below:
(i)	I don’t believe this book at all.

Here the verb is selecting for propositional content, which is present by exploitation in the dot object of the direct object.

For the present discussion, we ignore selection of a dot object in an artifactual type context. In general, the analysis will follow the introduction rule seen in (71a) below, but there are complications in some cases. These are discussed in Pustejovsky (2011).

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Titel: Type Theory and Lexical Decomposition
verfasst von: James Pustejovsky
Verlag: Springer Netherlands
Buch: Advances in Generative Lexicon Theory
Print ISBN: 978-94-007-5188-0

Electronic ISBN: 978-94-007-5189-7

Copyright-Jahr: 2013
DOI: https://doi.org/10.1007/978-94-007-5189-7_2

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