PosNet:Position-based Causal Relation Extraction Network

Abstract

Causal relation extraction is a natural language processing technology to extract causal entity pairs from text,which is widely used in financial,medical and other fields.Traditional causal relationship extraction technology needs to manually select text features for causal matching or use neural networks to extract features many times,resulting in complicated model structure and low extraction efficiency.To solve this problem,this paper proposes a position-based causal relation extraction network(PosNet) to improve the efficiency of causal relation extraction.Firstly,it preprocesses the text and constructs multi-granularity text features as the input of the network.Then passing the text features into the position prediction network,and predicting the start and end positions of causal entities by the classical shallow convolution neural network.Finally,the causal entities are assembled according to the start and end positions by the assembling algorithm,so that all causal entity pairs are extracted.Experimental results show that PosNet can improve the efficiency of causal relation extraction.

Keywords

• causal relation extraction|position information|text feature representation