TITLE:
Transliterated Word Identification and Application to Query Translation Mining
AUTHORS:
Jing Zhang, Lei Guo, Meiling Zhou, Jianmin Yao
KEYWORDS:
Transliteration, Query Classification, Supervised Learning, Translation Mining
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.2 No.2,
July
15,
2009
ABSTRACT: Query translation mining is a key technique in cross-language information retrieval and machine translation knowl-edge acquisition. For better performance, the queries are classified into transliterated words and non-transliterated words based on transliterated word identification model, and are further channeled to different mining processes. This paper is a pilot study on query classification for better translation mining performance, which is based on supervised classification and linguistic heuristics. The person name identification gets a precision of over 97%. Transliterated word translation mining shows satisfactory performance.