TY - GEN
T1 - Learning to cluster web search results
AU - Zeng, Hua-Jun
AU - He, Qi-Cai
AU - Chen, Zheng
AU - Ma, Wei-Ying
AU - Ma, Jinwen
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2004
Y1 - 2004
N2 - Organizing Web search results into clusters facilitates users' quick browsing through search results. Traditional clustering techniques are inadequate since they don't generate clusters with highly readable names. In this paper, we reformalize the clustering problem as a salient phrase ranking problem. Given a query and the ranked list of documents (typically a list of titles and snippets) returned by a certain Web search engine, our method first extracts and ranks salient phrases as candidate cluster names, based on a regression model learned from human labeled training data. The documents are assigned to relevant salient phrases to form candidate clusters, and the final clusters are generated by merging these candidate clusters. Experimental results verify our method's feasibility and effectiveness.
AB - Organizing Web search results into clusters facilitates users' quick browsing through search results. Traditional clustering techniques are inadequate since they don't generate clusters with highly readable names. In this paper, we reformalize the clustering problem as a salient phrase ranking problem. Given a query and the ranked list of documents (typically a list of titles and snippets) returned by a certain Web search engine, our method first extracts and ranks salient phrases as candidate cluster names, based on a regression model learned from human labeled training data. The documents are assigned to relevant salient phrases to form candidate clusters, and the final clusters are generated by merging these candidate clusters. Experimental results verify our method's feasibility and effectiveness.
KW - Document clustering
KW - Regression analysis
KW - Search result organization
UR - https://www.scopus.com/pages/publications/8644273327
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-8644273327&origin=recordpage
U2 - 10.1145/1008992.1009030
DO - 10.1145/1008992.1009030
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1581138814
SN - 9781581138818
T3 - Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 210
EP - 217
BT - Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
T2 - Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Y2 - 25 July 2004 through 29 July 2004
ER -