关键词: 关系抽取, 临床文本, 神经张量网络, 分段注意力机制, 样本不平衡

Abstract:

At present, biomedical relation extraction has made considerable progress. However, when dealt with complex clinical texts, due to the large number of long sentences and the high density distribution of entity pairs in the sentences, the existing methods of relation extraction still have defects. We propose a relation extraction model via tensor-based bidirectional gate recurrent unit (Tensor-BiGRU) and piecewise attention mechanism. The ability of BiGRU to extract the underlying features is enhanced based on tensor weight matrix. Two kinds of piecewise attention mechanisms are proposed to improve the performance of the model in capturing long sentence features.When the sentence has multiple entity pairs, the semantic representations of the entity pairs are introduced to overcome the performance degradation of the mode. A weight-adaptive cross-entropy loss function is proposed to improve the generalization of the model when the sample distribution of different relation categories in the dataset is unbalanced. The experimental results show that without relying on any feature engineering and high-performance computing environment, the model achieves advanced performance on the 2010 i2b2/VA clinical data set.

Key words: relation extraction, clinical texts, neural tensor network, piecewise attention mechanism, sample imbalance