Lexical Co-Occurrence and Contextual Window-Based Approach with Semantic Similarity for Query Expansion

Lexical Co-Occurrence and Contextual Window-Based Approach with Semantic Similarity for Query Expansion

Jagendra Singh (Jawaharlal Nehru University, School of Computer and System Sciences, New Delhi, India) and Rakesh Kumar (Jawaharlal Nehru University, School of Computer and System Sciences, New Delhi, India)
Copyright: © 2017 |Pages: 22
DOI: 10.4018/IJIIT.2017070104
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Abstract

Query expansion (QE) is an efficient method for enhancing the efficiency of information retrieval system. In this work, we try to capture the limitations of pseudo-feedback based QE approach and propose a hybrid approach for enhancing the efficiency of feedback based QE by combining corpus-based, contextual based information of query terms, and semantic based knowledge of query terms. First of all, this paper explores the use of different corpus-based lexical co-occurrence approaches to select an optimal combination of query terms from a pool of terms obtained using pseudo-feedback based QE. Next, we explore semantic similarity approach based on word2vec for ranking the QE terms obtained from top pseudo-feedback documents. Further, we combine co-occurrence statistics, contextual window statistics, and semantic similarity based approaches together to select the best expansion terms for query reformulation. The experiments were performed on FIRE ad-hoc and TREC-3 benchmark datasets. The statistics of our proposed experimental results show significant improvement over baseline method.
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Introduction

In this section, we present an overview of information retrieval, information retrieval system, and the need for query expansion. Further, it discusses appropriateness and drawbacks of term co-occurrence approaches for query expansion and the need for incorporating query terms context window and semantics in the field of automatic query expansion.

Information Retrieval

The discipline of information retrieval is almost as old as the computer itself. An old definition of information retrieval is the following by Mooers (1950):

Information retrieval is the name of the process or method whereby a prospective user of information is able to convert his need for information into an actual list of citations to documents in storage containing information useful to him.

An information retrieval system is a software program that is used to retrieve, store and manages needed information in a large collection. The system assists users to find the information need like the question answering system that returns the existence and location of documents instead of returning needed information or answer the question explicitly. Some system suggested documents may satisfy the user’s information need. These kinds of documents are called relevant documents. A perfect retrieval system would retrieve only the relevant documents, not the irrelevant documents. However, there are no perfect retrieval systems because the searching statements are necessarily incomplete, and relevance of documents is the user’s subjective opinion.

There are a large number of applications in which information retrieval is useful such as digital libraries, information filtering, recommender system, media search, search engines and many other and there is a constant need for improving such systems. In this context, information retrieval is an active field of research in computer science.

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