Iterative bilingual lexicon extraction from comparable corpora using a modified perceptron algorithm
Hong-Seok Kwon, Hyeong-Won Seo, Min-Ah Cheon, Jae‐Hoon Kim · Contemporary Engineering Sciences · 2014
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We present a novel iterative approach on bilingual lexicon extraction from comparable corpora. The approach is based on vector space model for word representation and a modified Perceptron algorithm. The approach requires a seed dictionary and a large amount of unlabeled training data. The seed dictionary is generated using the pivot-based approach and the unlabeled training data is dynamically labeled by the modified Perceptron algorithm using a similarity measure during learning process. In this paper, we extract bilingual lexicons by iteratively applying our proposed approach via the modified Perceptron algorithm. The empirical results have shown that our proposed approach significantly improves the accuracy for the top 1 candidate. In the future we will try to apply the multilayered Perceptron algorithm to our iterative approach for effective word representation.