Detecting Sub-Topic Correspondence through Bipartite Term Clustering


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

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Last time updated on 22/10/2014

This paper was published in CiteSeerX.

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