The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Learning to cluster web search results
Abstract
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.
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Cites
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Cited by
Exploiting query reformulations for web search result diversification
Santos, R. L. T., Macdonald, C., & Ounis, I. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772780 · Cited by 441
Extracting semantic relations from query logs
Baeza-Yates, R., & Tiberi, A. (2007) · Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1281192.1281204 · Cited by 208
Hybrid entity clustering using crowds and data
Lee, J., Cho, H., Park, J.-W., Cha, Y.-R., Hwang, S.-W., Nie, Z., & Wen, J.-R. (2013) · The VLDB Journal · doi:10.1007/s00778-013-0328-8 · Cited by 15
How fresh do you want your search results?
Cheng, S., Arvanitis, A., & Hristidis, V. (2013) · Proceedings of the 22nd ACM international conference on Information & Knowledge Management · doi:10.1145/2505515.2505696 · Cited by 11
Metadata
- Authors
- Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., & Ma, J.
- Venue
- Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2004-07-25
- Pages
- 210-217
- OpenAlex
- W2110441437
- Cited by
- 587
- References
- 10
- FWCI
- 32.20
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., & Ma, J. (2004). Learning to cluster web search results. Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1008992.1009030
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