Literature DB >> 17958162

Extracting semantic representations from word co-occurrence statistics: a computational study.

John A Bullinaria1, Joseph P Levy.   

Abstract

The idea that at least some aspects of word meaning can be induced from patterns of word co-occurrence is becoming increasingly popular. However, there is less agreement about the precise computations involved, and the appropriate tests to distinguish between the various possibilities. It is important that the effect of the relevant design choices and parameter values are understood if psychological models using these methods are to be reliably evaluated and compared. In this article, we present a systematic exploration of the principal computational possibilities for formulating and validating representations of word meanings from word co-occurrence statistics. We find that, once we have identified the best procedures, a very simple approach is surprisingly successful and robust over a range of psychologically relevant evaluation measures.

Mesh:

Year:  2007        PMID: 17958162     DOI: 10.3758/bf03193020

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  31 in total

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8.  Target identification among known drugs by deep learning from heterogeneous networks.

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9.  Limiting factors for mapping corpus-based semantic representations to brain activity.

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Journal:  PLoS One       Date:  2013-03-19       Impact factor: 3.240

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