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Search results clustering based on a linear weighting method of similarity

  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The cluster of search results can facilitate users in finding the needed from massive information. But the effect of the traditional text clustering has been verified not good enough. Lingo Algorithm, which adopts LSI for clustering, generates candidate labels first, then distributes the documents, and forms the clusters finally. On the basis of Lingo Algorithm, this paper presents a linear weighted method of Single-Pass improvement, which integrates HowNet semantic similarity and cosine similarity, fuses and rediscovers clusters, and extracting the cluster labels. The experiments have showed that our method it achieves a good results in clusters in the form of purity and F-measure.

Original languageEnglish
Title of host publicationProceedings - 2011 International Conference on Asian Language Processing, IALP 2011
Pages123-126
Number of pages4
DOIs
StatePublished - 2011
Event2011 International Conference on Asian Language Processing, IALP 2011 - Penang, Malaysia
Duration: 15 Nov 201117 Nov 2011

Publication series

NameProceedings - 2011 International Conference on Asian Language Processing, IALP 2011

Conference

Conference2011 International Conference on Asian Language Processing, IALP 2011
Country/TerritoryMalaysia
CityPenang
Period15/11/1117/11/11

Keywords

  • Cosine similarity
  • Information retrieval
  • Lingo algorithm
  • Semantic similarity
  • Text clustering

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