Retrieving information from microblog using pattern mining and relevance feedback

Lau, Cher Han, Tao, Xiaohui , Tjondronegoro, Dian, & Li , Yuefeng (2012) Retrieving information from microblog using pattern mining and relevance feedback. Data and Knowledge Engineering, Lecture Notes in Computer Science, 7696, pp. 153-160.

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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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ID Code: 57374
Item Type: Journal Article
Refereed: Yes
Keywords: Information Systems applications
DOI: 10.1007/978-3-642-34679-8_15
ISSN: 0302-9743
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Springer-Verlag Berlin Heidelberg
Deposited On: 19 Feb 2013 01:37
Last Modified: 12 Jun 2013 15:32

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