Relevance Feedback-based Query Expansion Model using Ranks Combining and Word2Vec Approach

ABSTRACT

Query expansion is a well-known method for improving the performance of information retrieval systems. Pseudo-relevance feedback (PRF)-based query expansion is a type of query expansion approach that assumes the top-ranked retrieved documents are relevant. The addition of all the terms of PRF documents is not important or appropriate for expanding the original user query. Hence, the selection of proper expansion term is very important for improving retrieval system performance. Various individual query expansion term selection methods have been widely investigated for improving system performance. Every individual expansion term selection method has its own weaknesses and strengths. In order to minimize the weaknesses and utilizing the strengths of the individual method, we used multiple terms selection methods together. First, this paper explored the possibility of improving overall system performance by using individual query expansion terms selection methods. Further, ranks-aggregating method named Borda count is used for combining multiple query expansion terms selection methods. Finally, Word2vec approach is used to select semantically similar terms with query after applying Borda count rank combining approach. Our experimental results on both data-sets TREC and FIRE demonstrated that our proposed approaches achieved significant improvement over each individual terms selection method and other's related state-of-the-art method.

KEYWORDS:

• Borda count ranking method
• Feature/term selection methods
• Information retrieval system
• Query expansion
• Semantic similarity

ACKNOWLEDGMENTS

The authors are grateful to the University Grants Commission, New Delhi, India, for providing research scholarship during the tenure of the work.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Jagendra Singh

Jagendra Singh is a doctoral candidate at School of Computer and Systems Sciences, Jawaharlal Nehru University, India. He received his Master's degree in computer engineering from School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi. His research interest is in information retrieval system, natural language processing, opinion mining, text Mining and web Mining.

E-mail: [email protected]

Aditi Sharan

Aditi Sharan has been working as an assistant professor for the past 12 years at the School of Computer and Systems Sciences, Jawaharlal Nehru University, India. She has a doctoral degree in computer science. She is involved in teaching undergraduate and graduate courses like database management, information retrieval, data mining, natural language processing and semantic web. She has published several research papers in international conferences and journals of repute.

E-mail: [email protected]