Improving Named Entity Translation by Exploiting Comparable and Parallel Corpora

Translation of named entities (NEs), such as person, organization, country, and location names is very important for several natural language processing applications. It plays a vital role in applications like cross lingual information retrieval, and machine translation. Web and news documents introduce new named entities on regular basis. Those new names cannot be captured by ordinary machine translation systems. In this paper, we introduce a framework for extracting named entity translation pairs. The framework contains methods for exploiting both comparable and parallel corpora to generate a regularly updated list of named entity translation pairs. We evaluate the quality of the extracted translation pairs by showing that it improves the performance of a named entity translation system. We report results on the ACE 2007 Entity Translation (ET) pilot evaluation development set.