Recurrent neural networks with segment attention and entity description for relation extraction from clinical texts.

Abstract

At present, great progress has been achieved on the relation extraction for clinical texts, but we have noticed that the current models have great drawbacks when dealing with long sentences and multiple entities in a sentence. In this paper, we propose a novel neural network architecture based on Bidirectional Long Short-Term Memory Networks for relation classification. Firstly, we utilize a concat-attention mechanism for capturing the most important context words for relation extraction in a sentence. In addition, a segment attention mechanism is proposed to improve the performance of the model processing long sentences. Finally, a tensor-based entity description is used to overcome the performance degradation of the model when there are multiple entities in a sentence. The performance of the proposed model is evaluated on a part of the i2b2-2010 shared task clinical relation extraction dataset. The result indicates that our model can effectively overcome the above two problems and improve the F1-score by approximately 3% compared with baseline model.