Detecting sub-topic correspondence through bipartite term clustering

Zvika Marx, I. Dagan, Eli Shamir

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageAmerican English
Title of host publication37thAnnual Meeting of the Association for Computational Linguistics (ACL)
StatePublished - 1999

Bibliographical note

Place of conference:College Park, Maryland, USA


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