@inproceedings{871a0ad358a74e6ca70fa55589927ebb,
title = "Reranking with multiple features for better transliteration",
abstract = "Effective transliteration of proper names via grapheme conversion needs to find transliteration patterns in training data, and then generate optimized candidates for testing samples accordingly. However, the top-1 accuracy for the generated candidates cannot be good if the right one is not ranked at the top. To tackle this issue, we propose to rerank the output candidates for a better order using the averaged perceptron with multiple features. This paper describes our recent work in this direction for our participation in NEWS2010 transliteration evaluation. The official results confirm its effectiveness in English-Chinese bidirectional transliteration. {\textcopyright} 2010 Association for Computational Linguistics",
keywords = "transliteration, proper name, rerank, averaged perceptron",
author = "Yan Song and Chunyu Kit and Hai Zhao",
year = "2010",
month = jul,
language = "English",
isbn = "978-1-932432-78-7",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics",
pages = "62--65",
editor = "A Kumaran and Haizhou Li",
booktitle = "Proceedings of the 2010 Named Entities Workshop",
address = "United States",
note = "48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 ; Conference date: 11-07-2010 Through 16-07-2010",
}