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Clustering Web Search Results with Maximum Spanning Trees

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AI*IA 2011: Artificial Intelligence Around Man and Beyond (AI*IA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6934))

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Abstract

We present a novel method for clustering Web search results based on Word Sense Induction. First, we acquire the meanings of a query by means of a graph-based clustering algorithm that calculates the maximum spanning tree of the co-occurrence graph of the query. Then we cluster the search results based on their semantic similarity to the induced word senses. We show that our approach improves classical search result clustering methods in terms of both clustering quality and degree of diversification.

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Authors and Affiliations

  1. Dipartimento di Informatica, Sapienza Università di Roma, Via Salaria, 113, 00198, Roma, Italy

    Antonio Di Marco & Roberto Navigli

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  1. Antonio Di Marco
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  2. Roberto Navigli
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Editors and Affiliations

  1. Department of Chemical, Management, Computer, and Mechanical Engineering (DICGIM), University of Palermo, Viale delle Scienze, Edificio 6, 90128, Palermo, Italy

    Roberto Pirrone  & Filippo Sorbello  & 

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Di Marco, A., Navigli, R. (2011). Clustering Web Search Results with Maximum Spanning Trees. In: Pirrone, R., Sorbello, F. (eds) AI*IA 2011: Artificial Intelligence Around Man and Beyond. AI*IA 2011. Lecture Notes in Computer Science(), vol 6934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23954-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-23954-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23953-3

  • Online ISBN: 978-3-642-23954-0

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