: Improving Domain-Specific Word Alignment with a General Bilingual Corpus

In conventional word alignment methods, some employ statistical models or statistical measures, which need large-scale bilingual sentencealigned training corpora. Others employ dictionaries to guide alignment selection. However, these methods achieve unsatisfactory alignment results when performing word alignment on a small-scale domain-specific bilingual corpus without terminological lexicons. This paper proposes an approach to improve word alignment in a specific domain, in which only a small-scale domain-specific corpus is available, by adapting the word alignment information in the general domain to the specific domain. This approach first trains two statistical word alignment models with the large-scale corpus in the general domain and the small-scale corpus in the specific domain respectively, and then improves the domain-specific word alignment with these two models. Experimental results show a significant improvement in terms of both alignment precision and recall, achieving a ...

• Type: Conference
• Year: 2004
• Where: AMTA
• Authors: Hua Wu, Haifeng Wang
• Keywords: AMTA 2004, Conventional Word Alignment, Information Management, Specific Domain, Word Alignment