Applying VSM and LCS to develop an integrated text retrieval mechanism

Cheng Shiun Tasi, Yong Ming Zang, Chien Hung Liu, Yueh Min Huang

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Text retrieval has received a lot of attention in computer science. In the text retrieval field, the most widely-adopted similarity technique is using vector space models (VSM) to evaluate the weight of terms and using Cosine, Jaccard or Dice to measure the similarity between the query and the texts. However, these similarity techniques do not consider the effect of the sequence of the information. In this paper, we propose an integrated text retrieval (ITR) mechanism that takes the advantage of both VSM and longest common subsequence (LCS) algorithm. The key idea of the ITR mechanism is to use LCS to re-evaluate the weight of terms, so that the sequence and weight relationships between the query and the texts can be considered simultaneously. The results of mathematical analysis show that the ITR mechanism can increase the similarity on Jaccard and Dice similarity measurements when a sequential relationship exists between the query and the texts.

Original languageEnglish
Pages (from-to)3974-3982
Number of pages9
JournalExpert Systems With Applications
Volume39
Issue number4
DOIs
Publication statusPublished - 2012 Mar 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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