<?xml version="1.0" standalone="yes"?>
<Paper uid="P02-1051">
  <Title>Translating Named Entities Using Monolingual and Bilingual Resources</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> 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. null A system that specializes in translating named entities such as the one we describe here would be an important tool for many NLP applications. Statistical 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). null 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.</Paragraph>
    <Paragraph position="1"> 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 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.</Paragraph>
  </Section>
class="xml-element"></Paper>