Abstract
Lexical co- occurrence models of semantic memory form representations of the meaning of a word on the basis of the number of times that pairs of words occur near one another in a large body of text. These models offer a distinct advantage over models that require the collection of a large number of judgments from human subjects, since the construction of the representations can be completely automated. Unfortunately, word frequency, a well-known predictor of reaction time in several cognitive tasks, has a strong effect on the co- occurrence counts in a corpus. Two words with high frequency are more likely to occur together purely by chance than are two words that occur very infrequently. In this article, we examine a modification of a successful method for constructing semantic representations from lexical co- occurrence. We show that our new method eliminates the influence of frequency, while still capturing the semantic characteristics of words.
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This research was supported by grants from NSERC to L.B. We are grateful to Richard Caron for comments on an early version of the manuscript.
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Durda, K., Buchanan, L. WINDSOR: Windsor improved norms of distance and similarity of representations of semantics. Behavior Research Methods 40, 705–712 (2008). https://doi.org/10.3758/BRM.40.3.705
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DOI: https://doi.org/10.3758/BRM.40.3.705