Combining Position-aware CNN and RNN for Relation Extraction

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Recurrent neural network (RNN) is often used for relation extraction that is based on long range dependency analyses and connections between nodes within the given sentences. However, RNN has a weaker ability in extracting certain important n-grams from the given datasets compared with convolutional neural network (CNN). Therefore, the combination of CNN and RNN becomes necessary for higher quality of relation classification. We propose Position-aware CNN (PA-CNN) which incorporates position information into CNN through an attention mechanism. The proposed PA-CNN is able to capture important linguistic clues for relation extraction effectively. Furthermore, we combine Position-aware CNN and RNN to take the benefits of both CNN and RNN to improve the relation extraction performance. Experiments on TACRED, a complex relation extraction dataset, show that our proposed Position-aware CNN outperforms traditional CNN, and the combinations of CNN and RNN outperform single CNN or RNN models for relation extraction.