Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction

  • Yu M
  • Gormley M
  • Dredze M
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Abstract

Compositional embedding models build a rep-resentation for a linguistic structure based on its component word embeddings. While re-cent work has combined these word embed-dings with hand crafted features for improved performance, it was restricted to a small num-ber of features due to model complexity, thus limiting its applicability. We propose a new model that conjoins features and word em-beddings while maintaing a small number of parameters by learning feature embeddings jointly with the parameters of a compositional model. The result is a method that can scale to more features and more labels, while avoiding overfitting. We demonstrate that our model at-tains state-of-the-art results on ACE and ERE fine-grained relation extraction.

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  • PUI: 608902457
  • ISBN: 9781941643495
  • SGR: 84960113164
  • SCOPUS: 2-s2.0-84960113164

Authors

  • Mo Yu

  • Matthew R Gormley

  • Mark Dredze

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