Detecting Sub-Topic Correspondence through Bipartite Term Clustering
| dc.creator | Marx, Zvika | |
| dc.creator | Dagan, Ido | |
| dc.creator | Shamir, Eli | |
| dc.date | 1999-08-01 | |
| dc.date.accessioned | 2026-07-07T03:24:17Z | |
| dc.date.available | 2026-07-07T03:24:17Z | |
| dc.description | 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. | |
| dc.description | html with 3 gif figures; generated from 7 pages MS-Word file | |
| dc.identifier | https://arxiv.org/abs/cs/9908001 | |
| dc.identifier | http://arxiv.org/abs/cs/9908001 | |
| dc.identifier | Proceedings of ACL'99 Workshop on Unsupervised Learning in Natural Language Processing, 1999, pp 45-51 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/33266 | |
| dc.subject | Computation and Language | |
| dc.subject | I.2.6, I.2.7, H.3.1 | |
| dc.title | Detecting Sub-Topic Correspondence through Bipartite Term Clustering | |
| dc.type | text |