Novel target attention convolutional neural network for relation classification

Abstract

Relation classification (RC) is an essential task in natural language processing (NLP), which extracts relationships of entity pairs in sentences of text. In the paper, a novel target attention convolutional neural network (TACNN) is proposed for the RC by fully utilizing word embedding information and position embedding information. Simultaneously, a target attention mechanism (TAM) is applied into a context layer of the convolutional neural network (CNN) model, which increases the effect of the relationship matrix weights of two entities in the sentence, while ignoring the calculation of irrelevant terms. And the TACNN is essentially to modify the weight of the relationship matrix of entities in the sentence at the context layer and connect the relationship feature composed of the lexical layer feature with the target attention layer feature. Therefore, the TACNN simplifies the structure of the CNN and improves the computational efficiency. On SemEval-2010 Task 8 dataset and Conll04 dataset, the TACNN obtains 85.3% and 71.4% of the F1-score, respectively. In contrast to previously available public models, the TACNN achieves a state-of-the-art level in the F1-score of the RC.