Context-Sensitive Measurement of Word Distance by Adaptive Scaling of a Semantic Space
<|reference_start|>Context-Sensitive Measurement of Word Distance by Adaptive Scaling of a Semantic Space: 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.<|reference_end|>
@article{kozima1996context-sensitive,
    title={Context-Sensitive Measurement of Word Distance by Adaptive Scaling of a
  Semantic Space},
    author={Hideki Kozima, Akira Ito (Communications Research Laboratory, Japan)},
    journal={arXiv preprint arXiv:cmp-lg/9601007},
    year={1996},
    archivePrefix={arXiv},
    eprint={cmp-lg/9601007},
    primaryClass={cmp-lg cs.CL}
}