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Title: Word Space Models of Semantic Similarity and Relatedness
Authors: Peirsman, Yves
Issue Date: Aug-2008
Publisher: Springer
Host Document: Proceedings of the 13th ESSLLI Student Session pages:143-152
Conference: European Summer School in Logic, Language and Information (ESSLLI) edition:20 location:Hamburg, Germany date:4-15 August 2008
Abstract: Word Space Models provide a convenient way of modelling word meaning in terms of a word’s contexts in a corpus. This paper investigates the influence of the type of context features on the kind of semantic information that the models capture. In particular, we make a distinction between semantic similarity and semantic relatedness. It is shown that the strictness of the context definition correlates with the models’ ability to identify semantically similar words: syntactic approaches perform
better than bag-of-word models, and small context windows are better than larger ones. For semantic relatedness, however, syntactic features and small context windows
are at a clear disadvantage. Second-order bag-of-word models perform below average across the board.
ISBN: 9783642147289
VABB publication type: VABB-5
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Quantitative Lexicology and Variational Linguistics (QLVL), Leuven

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