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An Information Theoretic Approach to Bilingual Word Clustering

Manaal Faruqui and Chris Dyer

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013


We present an information theoretic objective for bilingual word clustering that incorporates both monolingual distributional evidence as well as cross-lingual evidence from parallel corpora to learn high quality word clusters jointly in any number of languages. The monolingual component of our objective is the average mutual information of adjacent clusters in each language, while the bilingual component is the average mutual information of the aligned clusters. To evaluate our method, we use the word clusters in an NER system and demonstrate a statistically significant improvement in F1 score when using bilingual word clusters instead of monolingual clusters.

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