Detecting Sub-Topic Correspondence through Bipartite Term Clustering

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This paper addresses a novel task of detecting sub-topic correspondence in a pair of text fragments, enhancing common notions of text similarity. This task is addressed by coupling corresponding term subsets through bipartite clustering. The paper presents a cost-based clustering scheme and compares it with a bipartite version of the single-link method, providing illustrating results.
html with 3 gif figures; generated from 7 pages MS-Word file

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Computation and Language, I.2.6, I.2.7, H.3.1

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http://salesiana.dossiersoluciones.com/handle/123456789/33266

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/9908001
 http://arxiv.org/abs/cs/9908001
 Proceedings of ACL'99 Workshop on Unsupervised Learning in Natural Language Processing, 1999, pp 45-51

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arXiv