What Can Linguistics Contribute to Event Extraction?

Charles J. Fillmore, Srini Narayanan, Collin F. Baker

This paper examines the question of how a linguistic analysis of a written document can contribute to identifying, tracking and populating the eventualities that are presented in the document, either directly or indirectly, and representing degrees of belief concerning them. It is our view that the role of lexical analysis (as exemplified in the research carried out in the FrameNet project) is greater than usually assumed, so this paper is partly an attempt to clarify the boundary between on the one hand the information that can be derived on the basis of linguistic knowledge alone (composed of lexical meanings and the meanings of grammatical constructions) and on the other hand, reasoning based on beliefs about the source of a document, world knowledge, and common sense. Since the general linguistic processes described in this paper will apply to eventualities in general (by which we mean acts, happenings, states of affairs, and relations, whether real, proposed, imagined, or denied), our presentation will emphasize the linguistic processes themselves. In particular, we show that the kind of information produced by the lexicon-building project FrameNet can have a special role in contributing to text understanding, starting from the basic facts of the combinatorial properties of frame-bearing words (verbs, nouns, adjectives and prepositions) and arriving at the means of recognizing the anaphoric properties of specific unexpressed event participants, for all parts of speech, in defining a new layer of anaphora resolution and text cohesion. Using as a starting point the challenge text presented in the call for this workshop, we show the points at which a thorough linguistic analysis can articulate with the kind of simulation formalism demonstrated in X-schema diagrams, which themselves incorporate a great deal of world knowledge connected with the events introduced in the Hijacking text.

Subjects: 13. Natural Language Processing; 10. Knowledge Acquisition

Submitted: May 18, 2006

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