@inproceedings{f86c586cbb9e4affbed39b9989c40c11,
title = "Learning to rank for determining relevant document in Indonesian-English cross language information retrieval using BM25",
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%.",
author = "Syandra Sari and Mima Adriani",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014 ; Conference date: 18-10-2014 Through 19-10-2014",
year = "2014",
month = mar,
day = "23",
doi = "10.1109/ICACSIS.2014.7065896",
language = "English",
series = "Proceedings - ICACSIS 2014: 2014 International Conference on Advanced Computer Science and Information Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "309--314",
booktitle = "Proceedings - ICACSIS 2014",
address = "United States",
}