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- Title
Multi‐scale event causality extraction via simultaneous knowledge‐attention and convolutional neural network.
- Authors
Yu, Xiaoxiao; Wang, Xinzhi; Luo, Xiangfeng; Gao, Jianqi
- 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.
- Subjects
CONVOLUTIONAL neural networks; NATURAL language processing; RANDOM fields; NEURAL circuitry
- Publication
Expert Systems, 2022, Vol 39, Issue 5, p1
- ISSN
0266-4720
- Publication type
Article
- DOI
10.1111/exsy.12952