Retrieving Information from Microblog Using Pattern Mining and Relevance Feedback

  • Cher Han Lau
  • Xiaohui Tao
  • Dian Tjondronegoro
  • Yuefeng Li
Conference paper

DOI: 10.1007/978-3-642-34679-8_15

Volume 7696 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Lau C.H., Tao X., Tjondronegoro D., Li Y. (2012) Retrieving Information from Microblog Using Pattern Mining and Relevance Feedback. In: Xiang Y., Pathan M., Tao X., Wang H. (eds) Data and Knowledge Engineering. Lecture Notes in Computer Science, vol 7696. Springer, Berlin, Heidelberg

Abstract

Retrieving information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cher Han Lau
    • 1
  • Xiaohui Tao
    • 2
  • Dian Tjondronegoro
    • 1
  • Yuefeng Li
    • 1
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.University of Southern QueenslandDarling HeightsAustralia