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Reseach Article

Query Expansion Strategy based on Pseudo Relevance Feedback and Term Weight Scheme for Monolingual Retrieval

by Rekha Vaidyanathan, Sujoy Das, Namita Srivastava
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 8
Year of Publication: 2014
Authors: Rekha Vaidyanathan, Sujoy Das, Namita Srivastava
10.5120/18394-9646

Rekha Vaidyanathan, Sujoy Das, Namita Srivastava . Query Expansion Strategy based on Pseudo Relevance Feedback and Term Weight Scheme for Monolingual Retrieval. International Journal of Computer Applications. 105, 8 ( November 2014), 1-6. DOI=10.5120/18394-9646

@article{ 10.5120/18394-9646,
author = { Rekha Vaidyanathan, Sujoy Das, Namita Srivastava },
title = { Query Expansion Strategy based on Pseudo Relevance Feedback and Term Weight Scheme for Monolingual Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 8 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number8/18394-9646/ },
doi = { 10.5120/18394-9646 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:08.308009+05:30
%A Rekha Vaidyanathan
%A Sujoy Das
%A Namita Srivastava
%T Query Expansion Strategy based on Pseudo Relevance Feedback and Term Weight Scheme for Monolingual Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 8
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Query Expansion using Pseudo Relevance Feedback is a useful and a popular technique for reformulating the query. In our proposed query expansion method, we assume that relevant information can be found within a document near the central idea. The document is normally divided into sections, paragraphs and lines. The proposed method tries to extract keywords that are closer to the central theme of the document. The expansion terms are obtained by equi-frequency partition of the documents obtained from pseudo relevance feedback and by using tf-idf scores. The idf factor is calculated for number of partitions in documents. The group of words for query expansion is selected using the following approaches: the highest score, average score and a group of words that has maximum number of keywords. As each query behaved differently for different methods, the effect of these methods in selecting the words for query expansion is investigated. From this initial study, we extend the experiment to develop a rule-based statistical model that automatically selects the best group of words incorporating the tf-idf scoring and the 3 approaches explained here, in the future. The experiments were performed on FIRE 2011 Adhoc Hindi and English test collections on 50 queries each, using Terrier as retrieval engine.

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Index Terms

Computer Science
Information Sciences

Keywords

Pseudo Relevance Feedback Query Expansion tf-idf equi-frequency partition