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      Learning Crosslingual Word Embeddings without Bilingual Corpora

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          Abstract

          Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools. However, previous attempts had expensive resource requirements, difficulty incorporating monolingual data or were unable to handle polysemy. We address these drawbacks in our method which takes advantage of a high coverage dictionary in an EM style training algorithm over monolingual corpora in two languages. Our model achieves state-of-the-art performance on bilingual lexicon induction task exceeding models using large bilingual corpora, and competitive results on the monolingual word similarity and cross-lingual document classification task.

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          Author and article information

          Journal
          2016-06-30
          Article
          1606.09403
          a7933ffc-7f96-49e4-a9b8-a6634a3155db

          http://creativecommons.org/licenses/by-sa/4.0/

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          cs.CL cs.AI

          Theoretical computer science,Artificial intelligence
          Theoretical computer science, Artificial intelligence

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