Query Expansion for Effective Retrieval Results of Hindi–English Cross-Lingual IR. Issue 7 (7th June 2019)

• Record Type: Journal Article
• Authors: Chandra, Ganesh; Dwivedi, Sanjay K.
• Is Part Of: Applied artificial intelligence. Volume 33:Issue 7(2019)
• Page Start: 567
• Page End: 593
• Publication Date: 2019-06-07
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2019.1577018
• Languages: English
• ISSNs: 0883-9514

Abstract

ABSTRACT: Information retrieval (IR) is the science of identifying documents or sub-documents from a collection of information or database. The collection of information does not necessarily be available in only one language as information does not depend on languages. Monolingual IR is the process of retrieving information in query language whereas cross-lingual information retrieval (CLIR) is the process of retrieving information in a language that differs from query language. In current scenario, there is a strong demand of CLIR system because it allows the user to expand the international scope of searching a relevant document. As compared to monolingual IR, one of the biggest problems of CLIR is poor retrieval performance that occurs due to query mismatching, multiple representations of query terms and untranslated query terms. Query expansion (QE) is the process or technique of adding related terms to the original query for query reformulation. Purpose of QE is to improve the performance and quality of retrieved information in CLIR system. In this paper, QE has been explored for a Hindi–English CLIR in which Hindi queries are used to search English documents. We used Okapi BM25 for documents ranking, and then by using term selection value, translated queries have been expanded. All experiments have been performed using FIRE 2012 dataset. Our result shows that the relevancy of Hindi–English CLIR can be improved by adding the lowest frequency term.