Evaluating Features for Identifying Japanese-Chinese Bilingual Synonymous Technical Terms from Patent Families

Abstract

In the process of translating patent documents, a bilingual lexicon of technical terms is inevitable knowledge source. It is important to develop techniques of acquiring technical term translation equivalent pairs automatically from parallel patent documents. We take an approach of utilizing the phrase table of a state-of-the-art phrase-based statistical machine translation model. First, we collect candidates of synonymous translation equivalent pairs from parallel patent sentences. Then, we apply the Support Vector Machines (SVMs) to the task of identifying bilingual synonymous technical terms. This paper especially focuses on the issue of examining the effectiveness of each feature and identifies the minimum number of features that perform as comparatively well as the optimal set of features. Finally, we achieve the performance of over 90% precision with the condition of more than or equal to 25% recall.