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Context-Sensitive Measurement of Word Distance by Adaptive Scaling of a Semantic Space

Hideki Kozima·Akira Ito
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Abstract

The paper proposes a computationally feasible method for measuring context-sensitive semantic distance between words. The distance is computed by adaptive scaling of a semantic space. In the semantic space, each word in the vocabulary V is represented by a multi-dimensional vector which is obtained from an English dictionary through a principal component analysis. Given a word set C which specifies a context for measuring word distance, each dimension of the semantic space is scaled up or down according to the distribution of C in the semantic space. In the space thus transformed, distance between words in V becomes dependent on the context C. An evaluation through a word prediction task shows that the proposed measurement successfully extracts the context of a text.

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Hideki Kozima, Akira Ito. 1996-06-25. Context-Sensitive Measurement of Word Distance by Adaptive Scaling of a Semantic Space. https://arxiv.org/abs/cmp-lg/9601007

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