Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Identifying word translations in non-parallel texts
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Automatic identification of word translations from unrelated English and German corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning a translation lexicon from monolingual corpora
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Utilizing contextually relevant terms in bilingual lexicon extraction
UMSLLS '09 Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics
BUCC '11 Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web
Bootstrapping bilingual lexicons from comparable corpora for closely related languages
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Statistical Extraction and Comparison of Pivot Words for Bilingual Lexicon Extension
ACM Transactions on Asian Language Information Processing (TALIP)
Bilingual lexicon extraction from comparable corpora using label propagation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Many existing methods for bilingual lexicon learning from comparable corpora are based on similarity of context vectors. These methods suffer from noisy vectors that greatly affect their accuracy. We introduce a method for filtering this noise allowing highly accurate learning of bilingual lexicons. Our method is based on the notion of in-domain terms which can be thought of as the most important contextually relevant words. We provide a method for identifying such terms. Our evaluation shows that the proposed method can learn highly accurate bilingual lexicons without using orthographic features or a large initial seed dictionary. In addition, we also introduce a method for measuring the similarity between two words in different languages without requiring any initial dictionary.