Paper: A Phrase-Based Context-Dependent Joint Probability Model for Named Entity Translation

ACL ID I05-1053
Title A Phrase-Based Context-Dependent Joint Probability Model for Named Entity Translation
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

We propose a phrase-based context-dependent joint probability model for Named Entity (NE) translation. Our proposed model consists of a lexical mapping model and a permutation model. Target phrases are generated by the context-dependent lexical mapping model, and word reordering is per- formed by the permutation model at the phrase level. We also present a two- step search to decode the best result from the models. Our proposed model is evaluated on the LDC Chinese-English NE translation corpus. The experiment results show that our proposed model is high effective for NE translation.