Non-distributional Word Vector Representations

@inproceedings{Faruqui2015NondistributionalWV,
  title={Non-distributional Word Vector Representations},
  author={Manaal Faruqui and Chris Dyer},
  booktitle={ACL},
  year={2015}
}
Data-driven representation learning for words is a technique of central importance in NLP. While indisputably useful as a source of features in downstream tasks, such vectors tend to consist of uninterpretable components whose relationship to the categories of traditional lexical semantic theories is tenuous at best. We present a method for constructing interpretable word vectors from hand-crafted linguistic resources like WordNet, FrameNet etc. These vectors are binary (i.e, contain only 0 and… CONTINUE READING

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