Learning to rank for determining relevant document in Indonesian-English cross language information retrieval using BM25

Syandra Sari, Mirna Adriani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

One important task in cross-language information retrieval (CLIR) is to determine the relevance of a document from a number of documents based on user query. In this paper we applied pointwise learning to rank in SVM (Support Vector Machine) to determine the relevance of a document and used BM25 (Best Match 25) ranking function for selecting words as features. We did the experiment in Indonesian-English CLIR The results show an average ability of SVM to identify relevant documents is 88.51%, while the average accuracy of SVM to identify non relevant documents is 88%.

Original languageEnglish
Title of host publicationProceedings - ICACSIS 2014
Subtitle of host publication2014 International Conference on Advanced Computer Science and Information Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages309-314
Number of pages6
ISBN (Electronic)9781479980758
DOIs
Publication statusPublished - 23 Mar 2014
Event2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014 - Jakarta, Indonesia
Duration: 18 Oct 201419 Oct 2014

Publication series

NameProceedings - ICACSIS 2014: 2014 International Conference on Advanced Computer Science and Information Systems

Conference

Conference2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014
Country/TerritoryIndonesia
CityJakarta
Period18/10/1419/10/14

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