Abstract
This chapter describes two studies concerning automatic extraction of translation knowledge from parallel corpora. In the first study, we use statistically probable dependency relations to acquire word and phrasal correspondences. We obtained 90% precision using an English-Japanese parallel corpus of 9268 sentences in the business domain. The result showed that statistically probable dependency relations are effective in translation knowledge acquisition even for language pairs with different word ordering.
The second study compares three models of translation units, each of which uses different linguistic information: word segmentation, chunk boundaries, and word dependencies. The study investigates the relationship between the linguistic clues applied and translation knowledge extracted. We found that chunk boundaries are useful linguistic clues in extracting compound NPs which will be effective for extracting bilingual lexicons in the new domain. Furthermore, word dependencies are also useful for longer translation pairs such as idiomatic expressions. We demonstrate that using statistical NLP tools, in particular, statistical dependency parsers, offers robustness to the approach as well as valuable linguistic clues that work effectively in extracting translation knowledge from parallel corpora of languages from different families.
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Yamamoto, K., Matsumoto, Y. (2003). Extracting Translation Knowledge from Parallel Corpora. In: Carl, M., Way, A. (eds) Recent Advances in Example-Based Machine Translation. Text, Speech and Language Technology, vol 21. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0181-6_13
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DOI: https://doi.org/10.1007/978-94-010-0181-6_13
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