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Query Expansion based on Central Tendency and PRF for Monolingual Retrieval

Query Expansion based on Central Tendency and PRF for Monolingual Retrieval

Rekha Vaidyanathan, Sujoy Das, Namita Srivastava
Copyright: © 2016 |Volume: 6 |Issue: 4 |Pages: 21
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781466692510|DOI: 10.4018/IJIRR.2016100103
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MLA

Vaidyanathan, Rekha, et al. "Query Expansion based on Central Tendency and PRF for Monolingual Retrieval." IJIRR vol.6, no.4 2016: pp.30-50. http://doi.org/10.4018/IJIRR.2016100103

APA

Vaidyanathan, R., Das, S., & Srivastava, N. (2016). Query Expansion based on Central Tendency and PRF for Monolingual Retrieval. International Journal of Information Retrieval Research (IJIRR), 6(4), 30-50. http://doi.org/10.4018/IJIRR.2016100103

Chicago

Vaidyanathan, Rekha, Sujoy Das, and Namita Srivastava. "Query Expansion based on Central Tendency and PRF for Monolingual Retrieval," International Journal of Information Retrieval Research (IJIRR) 6, no.4: 30-50. http://doi.org/10.4018/IJIRR.2016100103

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Abstract

Query Expansion is the process of selecting relevant words that are closest in meaning and context to that of the keyword(s) of query. In this paper, a statistical method of automatically selecting contextually related words for expansion, after identifying a pattern in their score, is proposed. Words appearing in top 10 relevant document is given a score w.r.t partitions they appear in. Proposed statistical method, identifies a pattern of central tendency in the high scores and selects the right group of words for query expansion. The objective of the method is to keep the expanded query with minimum words (light), and still give statistically significant MAP values compared to the original query. Experimental results show 17-21% improvement of MAP over the original unexpanded query as baseline but achieves a performance similar to that of the state of the art query expansion models - Bo1 and KL. FIRE 2011 Adhoc English and Hindi data for 50 topics each were used for experiments with Terrier as the Retrieval Engine.

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