Corpus ID: 5831803

Combining Neural Networks and Log-linear Models to Improve Relation Extraction

@article{Nguyen2015CombiningNN,
  title={Combining Neural Networks and Log-linear Models to Improve Relation Extraction},
  author={T. Nguyen and R. Grishman},
  journal={ArXiv},
  year={2015},
  volume={abs/1511.05926}
}
The last decade has witnessed the success of the traditional feature-based method on exploiting the discrete structures such as words or lexical patterns to extract relations from text. Recently, convolutional and recurrent neural networks has provided very effective mechanisms to capture the hidden structures within sentences via continuous representations, thereby significantly advancing the performance of relation extraction. The advantage of convolutional neural networks is their capacity… Expand
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References

SHOWING 1-10 OF 59 REFERENCES
A Dependency-Based Neural Network for Relation Classification
A Convolutional Neural Network for Modelling Sentences
Classifying Relations by Ranking with Convolutional Neural Networks
Chain Based RNN for Relation Classification
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
...
1
2
3
4
5
...