期刊
BEHAVIOR RESEARCH METHODS
卷 44, 期 3, 页码 890-907出版社
SPRINGER
DOI: 10.3758/s13428-011-0183-8
关键词
Semantic representation; Corpus statistics; SVD
In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors-namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)-that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-theart performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.
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