Extracting Parallel Texts from the Web

Abstract

Parallel corpus is the valuable resource for some important applications of natural language processing such as statistical machine translation, dictionary construction, cross-language information retrieval. The Web is a huge resource of knowledge, which partly contains bilingual information in various kinds of web pages. It currently attracts many studies on building parallel corpora based on the Internet resource. However, obtaining a parallel corpus with high accuracy is still a challenge. This paper focuses on extracting parallel texts from bilingual web-sites of the English and Vietnamese language pair. We first propose a new way of designing content-based features, and then combining them with structural features under a framework of machine learning. In the experiment we obtain 88.2% of precision for the extracted parallel texts.