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Clustering Web Search Results with Maximum Spanning Trees

  • Antonio Di Marco
  • Roberto Navigli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6934)

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.

Keywords

Information Retrieval Word Sense Rand Index Document Cluster Lexical Ambiguity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio Di Marco
    • 1
  • Roberto Navigli
    • 1
  1. 1.Dipartimento di InformaticaSapienza Università di RomaRomaItaly

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