Query Expansion for Effective Retrieval Results of Hindi–English Cross-Lingual IR. Issue 7 (7th June 2019)
- Record Type:
- Journal Article
- Title:
- Query Expansion for Effective Retrieval Results of Hindi–English Cross-Lingual IR. Issue 7 (7th June 2019)
- Main Title:
- Query Expansion for Effective Retrieval Results of Hindi–English Cross-Lingual IR
- Authors:
- Chandra, Ganesh
Dwivedi, Sanjay K. - 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.
- Is Part Of:
- Applied artificial intelligence. Volume 33:Issue 7(2019)
- Journal:
- Applied artificial intelligence
- Issue:
- Volume 33:Issue 7(2019)
- Issue Display:
- Volume 33, Issue 7 (2019)
- Year:
- 2019
- Volume:
- 33
- Issue:
- 7
- Issue Sort Value:
- 2019-0033-0007-0000
- Page Start:
- 567
- Page End:
- 593
- Publication Date:
- 2019-06-07
- Subjects:
- Artificial intelligence -- Periodicals
006.3 - Journal URLs:
- http://www.tandfonline.com/toc/uaai20/current ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/08839514.2019.1577018 ↗
- Languages:
- English
- ISSNs:
- 0883-9514
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1571.650000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 10093.xml