Abstract
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 performed 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.
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Zhang, M., Li, H., Su, J., Setiawan, H. (2005). A Phrase-Based Context-Dependent Joint Probability Model for Named Entity Translation. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_53
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DOI: https://doi.org/10.1007/11562214_53
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