CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification

Relation classification is an important fundamental task in information extraction, and convolutional neural networks have been commonly applied to relation classification with good results. In recent years, due to the proposed pre-training model BERT, the use of which as a feature extraction architecture has become more and more popular, convolutional neural networks have gradually withdrawn from the stage of NLP, and the relation classification/extraction model based on pre-training BERT has achieved state-of-the-art results. However, none of these methods consider how to accurately capture the semantic features of the relationships between entities to reduce the number of noisy words in a sentence that are not helpful for relation classification. Moreover, these methods do not have a systematic prediction structure to fully utilize the extracted features for the relational classification task. To address these problems, a SpanBert-based relation classification model is proposed in this paper. Compared with existing Bert-based architectures, the model is able to understand the semantic information of the relationships between entities more accurately, and it can fully utilize the extracted features to represent the degree of dependency of a pair of entities with each type of relationship. In this paper, we design a feature fusion method called “SRS” (Strengthen Relational Semantics) and an attention-based prediction structure. Compared with existing methods, the feature fusion method proposed in this paper can reduce the noise interference of irrelevant words when extracting relational semantics, and the prediction structure proposed in this paper can make full use of semantic features for relational classification. We achieved advanced results on the SemEval-2010 Task 8 and the KBP37 relational dataset.