Title: A Web-based Unsupervised Algorithm for Learning Transliteration Model to Improve Translation of Low-Frequency Proper Names
Abstract: In machine translation, cross-language information retrieval, and cross-language question answering, the problems of unknown term translation are difficult to be solved. Although we have proposed several effective Web-based term translation extraction methods exploring Web resources to deal with translation of frequent Web query terms. However, many low-frequency unknown terms are still difficult to be translated by using our previous Web-based term translation extraction methods. Therefore, in this paper we propose a two-stage hybrid translation extraction method, which is composed of our pervious Web-based term translation extraction method and a new Web-based transliteration method to improve translation of low-frequency unknown proper names. Additionally, to construct a good quality transliteration model, we also present a Web-based unsupervised learning algorithm to automatically collect diverse English-Chinese transliteration pairs from the Web. Experimental results showed that our new method can make great improvements for translation of unknown proper names.
Publication Year: 2006
Publication Date: 2006-03-21
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 5
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot