Abstract
When mining parallel text from comparable corpora, we confront vast search space since parallel sentence or sub-sentential fragments can be scattered throughout the source and target corpus. To reduce the search space, most previous approaches have tried to use heuristics to mine comparable documents. However, these heuristics are only available in few cases. Instead, we go on a different direction and adopt the cross-language information retrieval (CLIR) framework to find translation candidates directly at sentence level from comparable corpus. What’s more, for the sake of better retrieval result, two simple but effective query expansion methods are proposed. Experimental results show that using our query expansion methods can help to improve the recall significantly and obtain candidates of sentence pairs with high quality. Thus, our methods can help to make good preparation for extracting both parallel sentences and fragments subsequently.
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Xiang, L., Zhou, Y., Hao, J., Zhang, D. (2014). Query Expansion for Mining Translation Knowledge from Comparable Data. In: Sun, M., Liu, Y., Zhao, J. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2014 2014. Lecture Notes in Computer Science(), vol 8801. Springer, Cham. https://doi.org/10.1007/978-3-319-12277-9_18
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DOI: https://doi.org/10.1007/978-3-319-12277-9_18
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