Parallel corpora are a valuable resource for tasks such as cross-language information retrieval and data-driven natural language processing systems. Previously only small scale corpora have been available, thus restricting their practical use. This paper describes a system that overcomes this limitation by automatically collecting high quality parallel bilingual corpora from the web. Previous systems used a single principle feature for parallel web page verification, whereas we use multiple features to identify parallel texts via a k-nearest-neighbor classifier. Our system was evaluated using a data set containing 6500 Chinese{ English candidate parallel pairs that have been manually annotated. Experiments show that the use of a k-nearest-neighbors classifier with multiple features achieves substantial improvements\u00a0 over the systems that use any one of these features. The system achieved a precision rate of 95% and a recall rate of 97%, and thus is a\u00a0 significant improvement over earlier work.<\/p>\n","protected":false},"excerpt":{"rendered":"
Parallel corpora are a valuable resource for tasks such as cross-language information retrieval and data-driven natural language processing systems. Previously only small scale corpora have been available, thus restricting their practical use. This paper describes a system that overcomes this limitation by automatically collecting high quality parallel bilingual corpora from the web. 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