Bi-GRU Relation Extraction Model Based on Keywords Attention

Abstract

Relational extraction plays an important role in the field of natural language processing to predict semantic relationships between entities in a sentence. Currently, most models have typically utilized the natural language processing tools to capture high-level features with an attention mechanism to mitigate the adverse effects of noise in sentences for the prediction results. However, in the task of relational classification, these attention mechanisms do not take full advantage of the semantic information of some keywords which have information on relational expressions in the sentences. Therefore, we propose a novel relation extraction model based on the attention mechanism with keywords, named Relation Extraction Based on Keywords Attention (REKA). In particular, the proposed model makes use of bi-directional GRU (Bi-GRU) to reduce computation, obtain the representation of sentences, and extracts prior knowledge of entity pair without any NLP tools. Besides the calculation of the entity-pair similarity, Keywords attention in the REKA model also utilizes a linear-chain conditional random field (CRF) combining entity-pair features, similarity features between entity-pair features, and its hidden vectors, to obtain the attention weight resulting from the marginal distribution of each word. Experiments demonstrate that the proposed approach can utilize keywords incorporating relational expression semantics in sentences without the assistance of any high-level features and achieve better performance than traditional methods.