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Conference Papers Year : 2013

Post-Retrieval Clustering Using Third-Order Similarity Measures

Abstract

Post-retrieval clustering is the task of clustering Web search results. Within this context, we propose a new methodology that adapts the classical K-means algorithm to a third-order similarity measure initially developed for NLP tasks. Results obtained with the definition of a new stopping criterion over the ODP-239 and the MORESQUE golden standard datasets evidence that our proposal outperforms all reported text-based approaches.
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Dates and versions

hal-00931263 , version 1 (19-09-2014)

Identifiers

  • HAL Id : hal-00931263 , version 1

Cite

José G. Moreno, Gaël Dias, Guillaume Cleuziou. Post-Retrieval Clustering Using Third-Order Similarity Measures. Annual Meeting of the Association for Computational Linguistics (ACL 2013), Aug 2013, Sofia, Bulgaria. pp.153-158. ⟨hal-00931263⟩
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