Charagram: Embedding Words and Sentences via Character n-grams

  title={Charagram: Embedding Words and Sentences via Character n-grams},
  author={J. Wieting and Mohit Bansal and Kevin Gimpel and Karen Livescu},
  • J. Wieting, Mohit Bansal, +1 author Karen Livescu
  • Published 2016
  • Computer Science
  • ArXiv
  • We present Charagram embeddings, a simple approach for learning character-based compositional models to embed textual sequences. A word or sentence is represented using a character n-gram count vector, followed by a single nonlinear transformation to yield a low-dimensional embedding. We use three tasks for evaluation: word similarity, sentence similarity, and part-of-speech tagging. We demonstrate that Charagram embeddings outperform more complex architectures based on character-level… CONTINUE READING
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