Translating Named Entities Using Monolingual and Bilingual Resources

Translating Named Entities Using Monolingual and Bilingual Resources Yaser Al-Onaizan and Kevin Knight Information Sciences Institute University of Southern California 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292 � yaser,knight � @isi.edu Abstract Named entity phrases are some of the most difficult phrases to translate because new phrases can appear from nowhere, and because many are domain specific, not to be found in bilingual dictionaries. We present a novel algorithm for translating named entity phrases using easily obtain- able monolingual and bilingual resources. We report on the application and evalua- tion of this algorithm in translating Arabic named entities to English. We also com- pare our results with the results obtained from human translations and a commer- cial system for the same task. 1 Introduction Named entity phrases are being introduced in news stories on a daily basis in the form of personal names, organizations, locations, temporal phrases, and monetary expressions. While the identifica- tion of named entities in text has received sig- nificant attention (e.g., Mikheev et al. (1999) and Bikel et al. (1999)), translation of named entities has not. This translation problem is especially challenging because new phrases can appear from nowhere, and because many named-entities are do- main specific, not to be found in bilingual dictionar- ies. A system that specializes in translating named en- tities such as the one we describe here would be an important tool for many NLP applications. Statisti- cal machine translation systems can use such a sys- tem as a component to handle phrase translation in order to improve overall translation quality. Cross- Lingual Information Retrieval (CLIR) systems could identify relevant documents based on translations of named entity phrases provided by such a sys- tem. Question Answering (QA) systems could ben- efit substantially from such a tool since the answer to many factoid questions involve named entities (e.g., answers to who questions usually involve Per- sons/Organizations, where questions involve Loca- tions, and when questions involve Temporal Ex- pressions). In this paper, we describe a system for Arabic- English named entity translation, though the tech- nique is applicable to any language pair and does not require especially difficult-to-obtain resources. The rest of this paper is organized as follows. In Section 2, we give an overview of our approach. In Section 3, we describe how translation candidates are generated. In Section 4, we show how mono- lingual clues are used to help re-rank the translation candidates list. In Section 5, we describe how the candidates list can be extended using contextual in- formation. We conclude this paper with the evalua- tion results of our translation algorithm on a test set. We also compare our system with human translators and a commercial system. 2 Our Approach The frequency of named-entity phrases in news text reflects the significance of the events they are associ- ated with. When translating named entities in news stories of international importance, the same event Computational Linguistics (ACL), Philadelphia, July 2002, pp. 400-408. Proceedings of the 40th Annual Meeting of …

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