tailieunhanh - Báo cáo khoa học: " Translating Named Entities Using Monolingual and Bilingual Resources"

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 obtainable monolingual and bilingual resources. We report on the application and evaluation of this algorithm in translating Arabic named entities to English. We also compare our results with the results obtained from human translations and a commercial system for the same task. . | Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics ACL Philadelphia July 2002 pp. 400-408. 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 @ 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 obtainable monolingual and bilingual resources. We report on the application and evaluation of this algorithm in translating Arabic named entities to English. We also compare our results with the results obtained from human translations and a commercial 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 identification of named entities in text has received significant attention . 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 domain specific not to be found in bilingual dictionaries. A system that specializes in translating named entities 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 system as a component to handle phrase translation in order to improve overall translation quality. CrossLingual Information Retrieval CLIR systems could identify relevant documents based on translations of named entity phrases provided by such a system. Question Answering QA systems could benefit substantially .