Multi‐scale event causality extraction via simultaneous knowledge‐attention and convolutional neural network
Xiaoxiao Yu, Xinzhi Wang, Xiangfeng Luo, Jianqi Gao · Expert Systems · 2022
Abstract Event causality extraction is a challenging task in natural language processing (NLP), which plays an important role in event prediction, scene generation, question answering and textual entailment. Most existing methods focus on extracting single‐scale (such as phrase) event causality, while fails to extract multi‐scale (such as word, phrase, sentence) event causality. To fill the gap, we propose multi‐scale event causality extraction via simultaneous knowledge‐attention and convolutional neural network (KA‐CNN). First, knowledge‐attention takes N‐gram embedding as input and takes semantic features, fused with prior knowledge through causal associative link network (CALN), as output. Second, multi‐scale CNN is designed with word embedding as input and semantic feature of corpus as output. Third, bidirectional long short‐term memory with conditional random field (BiLSTM + CRF) is conducted after concatenation of features from knowledge‐attention and multi‐scale CNN. Finally, we compare our results with other baselines. The experimental results show that our proposed method shows promising result in extracting multi‐scale event causality.