Abstract
In the literature, two main categories of methods have been proposed for bilingual lexicon extraction from comparable corpora, namely topic model and context based methods. In this paper, we present a bilingual lexicon extraction system that is based on a novel combination of these two methods in an iterative process. Our system does not rely on any prior knowledge and the performance can be iteratively improved. To the best of our knowledge, this is the first study that iteratively exploits both topical and contextual knowledge for bilingual lexicon extraction. Experiments conduct on Chinese–English and Japanese–English Wikipedia data show that our proposed method performs significantly better than a state–of–the–art method that only uses topical knowledge.
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Chu, C., Nakazawa, T., Kurohashi, S. (2014). Iterative Bilingual Lexicon Extraction from Comparable Corpora with Topical and Contextual Knowledge. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54903-8_25
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DOI: https://doi.org/10.1007/978-3-642-54903-8_25
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