TOWARDS COMPUTATIONAL DISCRIMINATION OF ENGLISH WORD SENSES (STATISTICAL, STOCHASTIC, DISCOURSE).
Item
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Title
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TOWARDS COMPUTATIONAL DISCRIMINATION OF ENGLISH WORD SENSES (STATISTICAL, STOCHASTIC, DISCOURSE).
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Identifier
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AAI8708276
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identifier
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8708276
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Creator
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BLACK, EZRA WILLIAM.
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Contributor
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John A. Moyne
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Date
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1987
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Language
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English
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Publisher
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City University of New York.
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Subject
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Language, Linguistics
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Abstract
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An experiment is conducted which compares three different methods of deciding which of three or four senses characterizes each occurrence of a word for which a Key Word In Context concordance has been constructed. The three methods consist of a dictionary-based approach (DG) where categories intended to classify the words and expressions occurring in each concordance line are simply the subject codes of a major dictionary; an approach (DS1) in which categories are obtained via a frequency analysis of words occurring in the immediate neighborhood of the "node word"--the word in focus--of the concordance, and of "content" words occurring anywhere in a given line; and an approach (DS2) chiefly based on the content-analytic categories obtained by closely reading the concordances of a 100-type sampling of words occurring in the 20-25-million-token English text source, consisting of the official proceedings of the Canadian House of Commons. Results are that DG performs extremely poorly--in fact, near-randomly; DS1 and DS2 yield better and substantially similar performances. The conclusion is that domain-general, syntax-based approaches to automatic word sense discrimination and domain-specific, content-analytic approaches need and complement each other.
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Type
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dissertation
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Source
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PQT Legacy CUNY.xlsx
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degree
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Ph.D.
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Program
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Linguistics
