Automatic Extraction of Named Entity Translingual Equivalence Based on Multi-Feature Cost Minimization

Abstract

Translingual equivalence refers to the relationship between expressions of the same meaning from different languages. Identifying translingual equivalence of named entities (NE) can significantly contribute to multilingual natural language processing, such as crosslingual information retrieval, crosslingual information extraction and statistical machine translation. In this paper we present an integrated approach to extract NE translingual equivalence from a parallel Chinese-English corpus.Starting from a bilingual corpus where NEs are automatically tagged for each language, NE pairs are aligned in order to minimize the overall multi-feature alignment cost. An NE transliteration model is presented and iteratively trained using named entity pairs extracted from a bilingual dictionary. The transliteration cost, combined with the named entity tagging cost and word-based translation cost, constitute the multi-feature alignment cost. These features are derived from several information sources using unsupervised and partly supervised methods. A greedy search algorithm is applied to minimize the alignment cost. Experiments show that the proposed approach extracts NE translingual equivalence with 81% F-score and improves the translation score from 7.68 to 7.74.