Enriching Word Vectors with Subword Information

@article{Bojanowski2016EnrichingWV,
  title={Enriching Word Vectors with Subword Information},
  author={Piotr Bojanowski and Edouard Grave and Armand Joulin and Tomas Mikolov},
  journal={Transactions of the Association for Computational Linguistics},
  year={2016},
  volume={5},
  pages={135-146}
}
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is… CONTINUE READING

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