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JACIII Vol.17 No.2 pp. 149-156
doi: 10.20965/jaciii.2013.p0149
(2013)

Paper:

Comparative Analysis of Relevance for SVM-Based Interactive Document Retrieval

Hiroshi Murata*, Takashi Onoda*, and Seiji Yamada**

*Central Research Institute of Electric Power Industry (CRIEPI), 2-11-1 Iwado kita, Komae-shi, Tokyo 201-8511, Japan

**National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

Received:
July 26, 2012
Accepted:
December 20, 2012
Published:
March 20, 2013
Keywords:
interactive document retrieval, support vector machines, relevance feedback, kernel method
Abstract

Support Vector Machines (SVMs) were applied to interactive document retrieval that uses active learning. In such a retrieval system, the degree of relevance is evaluated by using a signed distance from the optimal hyperplane. It is not clear, however, how the signed distance in SVMs has characteristics of vector space model. We therefore formulated the degree of relevance by using the signed distance in SVMs and comparatively analyzed it with a conventional Rocchio-based method. Although vector normalization has been utilized as preprocessing for document retrieval, few studies explained why vector normalization was effective. Based on our comparative analysis, we theoretically show the effectiveness of normalizing document vectors in SVM-based interactive document retrieval. We then propose a cosine kernel that is suitable for SVM-based interactive document retrieval. The effectiveness of the method was compared experimentally with conventional relevance feedback for Boolean, Term Frequency and Term Frequency-Inverse Document Frequency representations of document vectors. Experimental results for a Text REtrieval Conference data set showed that the cosine kernel is effective for all document representations, especially Term Frequency representation.

Cite this article as:
Hiroshi Murata, Takashi Onoda, and Seiji Yamada, “Comparative Analysis of Relevance for SVM-Based Interactive Document Retrieval,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.2, pp. 149-156, 2013.
Data files:
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