Using Lexical Expansion to Learn Inference Rules from Sparse Data

@inproceedings{Melamud2013UsingLE,
  title={Using Lexical Expansion to Learn Inference Rules from Sparse Data},
  author={Oren Melamud and Ido Dagan and Jacob Goldberger and Idan Szpektor},
  booktitle={ACL},
  year={2013}
}
Automatic acquisition of inference rules for predicates is widely addressed by computing distributional similarity scores between vectors of argument words. In this scheme, prior work typically refrained from learning rules for low frequency predicates associated with very sparse argument vectors due to expected low reliability. To improve the learning of such rules in an unsupervised way, we propose to lexically expand sparse argument word vectors with semantically similar words. Our… CONTINUE READING