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Abstract: We investigate four hierarchical clustering methods (single-link, complete-link, groupwise-average, and single-pass) and two linguistically motivated text features (noun phrase heads and proper names) in the context of document clustering. A statistical model for combining similarity information from multiple sources is described and applied to DARPA's Topic Detection and Tracking phase 2 (TDT2) data. This model, based on log-linear regression, alleviates the need for extensive search in order... (Update)
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BibTeX entry: (Update)
Vasileios Hatzivassiloglou, Luis Gravano, and Ankineedu Maganti. 2000. An investigation of linguistic features and clustering algorithms for topical document clustering. In Proceedings of the 23rd Annual ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-00), pages 224--231, Athens, Greece, July. http://citeseer.ist.psu.edu/hatzivassiloglou00investigation.html More
@inproceedings{ hatzivassiloglou00investigation,
author = "Vasileios Hatzivassiloglou and Luis Gravano and Ankineedu Maganti",
title = "An investigation of linguistic features and clustering algorithms for topical document clustering",
booktitle = "SIGIR 2000",
pages = "224-231",
year = "2000",
url = "citeseer.ist.psu.edu/hatzivassiloglou00investigation.html" }
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