A learning-based term-weighting approach for information retrieval

Authors:
GuangCan Liu;Yong Yu;Xing Zhu
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Year:
2005

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Abstract

One of the core components in information retrieval (IR) is the document-term-weighting scheme. In this paper, we will propose a novel learning-based term-weighting approach to improve the retrieval performance of vector space model in homogeneous collections. We first introduce a simple learning system to weighting the index terms of documents. Then, we deduce a formal computational approach according to some theories of matrix computation and statistical inference. Our experiments on 8 collections will show that our approach out-performs classic tfidf weighting, about 20%-45%.