Detecting Sub-Topic Correspondence through Bipartite Term Clustering
<|reference_start|>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.<|reference_end|>
@article{marx1999detecting,
    title={Detecting Sub-Topic Correspondence through Bipartite Term Clustering},
    author={Zvika Marx (1 and 2), Ido Dagan (1), Eli Shamir (2) ((1) Bar-Ilan
  University, (2) The Hebrew University of Jerusalem)},
    journal={Proceedings of ACL'99 Workshop on Unsupervised Learning in Natural
  Language Processing, 1999, pp 45-51},
    year={1999},
    archivePrefix={arXiv},
    eprint={cs/9908001},
    primaryClass={cs.CL}
}