Predication

In Latent Semantic Analysis (LSA) the meaning of a word is represented as a vector in a high‐dimensional semantic space. Different meanings of a word or different senses of a word are not distinguished. Instead, word senses are appropriately modified as the word is used in different contexts. In N‐V...

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Bibliographic Details
Published in:Cognitive science 2001-03, Vol.25 (2), p.173-202
Main Author: Kintsch, Walter
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Cognition & reasoning
Linguistics
Production and comprehension processes
Psycholinguistics
Psychology of language
Semantics
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Summary:In Latent Semantic Analysis (LSA) the meaning of a word is represented as a vector in a high‐dimensional semantic space. Different meanings of a word or different senses of a word are not distinguished. Instead, word senses are appropriately modified as the word is used in different contexts. In N‐VP sentences, the precise meaning of the verb phrase depends on the noun it is combined with. An algorithm is described to adjust the meaning of a predicate as it is applied to different arguments. In forming a sentence meaning, not all features of a predicate are combined with the features of the argument, but only those that are appropriate to the argument. Hence, a different “sense” of a predicate emerges every time it is used in a different context. This predication algorithm is explored in the context of four different semantic problems: metaphor interpretation, causal inferences, similarity judgments, and homonym disambiguation.
ISSN:0364-0213
1551-6709
DOI:10.1207/s15516709cog2502_1