We present a system for generating parsers based directly on the metaphor of parsing as deduction. Parsing algorithms can be represented directly as deduction systems, and a single deduction engine can interpret such deduction systems so as to implement the corresponding parser. The method generalizes easily to parsers for augmented phrase structure formalisms, such as definiteclause grammars and other logic grammar formalisms, and has been used for rapid prototyping of parsing algorithms for a variety of formalisms including variants of tree-adjoining grammars, categorial grammars, and lexicalized context-free grammars.

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Grammar formalisms based on the encoding of grammatical information in complex-valued feature systems enjoy some currency both in linguistics and natural-language-processing research. Such formalisms can be thought of by analogy to

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We describe a computational framework for a grammar architecture in which different linguistic domains such as morphology, syntax, and semantics are treated not as separate components but compositional domains. Word and phrase formation are modeled as uniform processes contributing to the derivation of the semantic form. The morpheme, as well as the lexeme, has lexical representation in the form of semantic content, tactical constraints, and phonological realization. The model is based on Combinatory Categorial Grammars. 1

ABSTRACT: We demonstrate ways to enhance the coverage of a symbolic NLP system through data-intensive and machine learning techniques, while preserving the advantages of using a principled symbolic grammar formalism. We automatically acquire a large syntactic CCG lexicon from the Penn Treebank and combine it with semantic and morphological infor-mation from another hand-built lexicon using decision tree and maximum entropy classifiers. We also integrate statistical preprocessing methods in our system.

Some problems are discussed that arise for incremental processing using certain flexible categorial grammars, which involve either undesirable parsing properties or failure to allow combinations useful to incrementality. We suggest a new calculus which, though `designed' in relation to categorial interpretations of some notions of dependency grammar, seems to provide a degree of flexibility that is highly appropriate for incremental interpretation. We demonstrate how this grammar may be used for efficient incremental parsing, by employing normalisation techniques. Introduction A range of categorial grammars (CGs) have been proposed which allow considerable flexibility in the assignment of syntactic structure, a characteristic which provides for categorial treatments of extraction (Ades & Steedman, 1982) and non-constituent coordination (Steedman, 1985; Dowty, 1988), and that is claimed to allow for incremental processing of natural language (Steedman, 1989). It is this latter possibil...

The idea that to perceive an object is to perceive its affordances---that is, the interactions of the perceiver with the world that the object supports or affords---is attractive from the point of view of theories in cognitive science that emphasize the fundamental role of actions in representing an agent's knowledge about the world. However, in this general form, the notion has so far lacked a formal expression. This paper offers a representation for objects in terms of their affordances using Linear Dynamic Event Calculus, a formalism for reasoning about causal relations over events. It argues that a representation of this kind, linking objects to the events which they are characteristically involved in, underlies some universal operations of natural language syntactic and semantic composition that are postulated in Combinatory Categorial Grammar (CCG). These observations imply that the language faculty is more directly related to prelinguistic cognitive apparatus used for planning action than formal theories in either domain have previously seemed to allow.