Chinese-english OOV term translation with web mining, Multiple feature fusion and supervised learning

Abstract

This paper focuses on the Web-based Chinese-English Out-of- Vocabulary (OOV) term translation pattern, and emphasizes on the translation selection based on multiple feature fusion and the ranking based on Ranking Support Vector Machine (Ranking SVM). By utilizing the SIGHAN2005 corpus for the Chinese Named Entity Recognition (NER) task and selected new terms, the experiments based on different data sources show the consistent results. From the experimental results for combining our model with Chinese- English Cross-Language Information Retrieval (CLIR) on the data sets of TREC, it can be found that the obvious performance improvements for both query translation and CLIR are obtained.