---
title: "Named Entity Translation with Web Mining and Transliteration"
authors:
  - "Long Jiang"
  - "Ming Zhou"
  - "Lee-Feng Chien"
  - "Cheng Niu"
doi: ""
url: "https://ijcai.org/proceedings/2007/263"
pdf: "https://ijcai.org/proceedings/2007/0263.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1629-1633"
track: ""
keywords:
---

# Named Entity Translation with Web Mining and Transliteration

Long Jiang, Ming Zhou, Lee-Feng Chien, Cheng Niu

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 1629-1633.

## Abstract

This paper presents a novel approach to improve the named entity translation by combining a translite-ration approach with web mining, using web in-formation as a source to complement translitera-tion, and using transliteration information to guide and enhance web mining. A Maximum Entropy model is employed to rank translation candidates by combining pronunciation similarity and bilingual contextual co-occurrence. Experimental results show that our approach effectively improves the precision and recall of the named entity translation by a large margin.