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 . 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 ( 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 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 . Cross-Lingual 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 from such a tool since the answer to many factoid questions involve named entities ( e.g. , answers to who questions usually involve Persons / Organizations , where questions involve Locations , and when questions involve Temporal Expressions ) . In this paper , we describe a system for Arabic-English named entity translation , though the technique 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 monolingual 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 information . We conclude this paper with the evaluation results of our translation algorithm on a test set . We also compare our system with human translators and a commercial system . We have presented a named entity translation algorithm that performs at near human translation accuracy when translating Arabic named entities to English . The algorithm uses very limited amount of hard-to-obtain bilingual resources and should be easily adaptable to other languages . We would like to apply to other languages such as Chinese and Japanese and to investigate whether the current algorithm would perform as well or whether new algorithms might be needed . Currently , our translation algorithm does not use any dictionary of named entities and they are translated on the fly . Translating a common name incorrectly has a significant effect on the translation accuracy . We would like to experiment with adding a small named entity translation dictionary for common names and see if this might improve the overall translation accuracy .

Challenge: Translating named entities is challenging since they can appear from nowhere, and cannot be found in bilingual dictionaries because they are domain specific.
Approach: They propose an algorithm for Arabic-English named entity translation which uses easily obtainable monolingual and bilingual resources and a limited amount of hard-to-obtain bilingual resources.
Outcome: The proposed algorithm is compared with human translators and a commercial system and it performs at near human translation.