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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.

Iterative bilingual lexicon extraction from comparable corpora using a modified perceptron algorithm