@inproceedings{Kozima1997,
abstract = {This paper proposes a computationally feasible method for measuring the 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 multidimensional vector which is extracted from an English dictionary through principal component analysis. Given a word set C which specifies a context, 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, the 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.},
author = {Kozima, Hideki and Ito, Akira},
booktitle = {Recent Advances in Natural Language Processing: Selected Papers from RANLP 1995, Volume 136 of Amsterdam Studies in the Theory and History of Linguistic Science: Current Issues in Linguistic Theory},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
pages = {111----124},
title = {{Context-Sensitive Word Distance by Adaptive Scaling of a Semantic Space}},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.2091},
year = {1997}
}