Combing Type-Aware Attention and Graph Convolutional Networks for Event Detection  

作　　者：Kun Ding Lu Xu Ming Liu Xiaoxiong Zhang Liu Liu Daojian Zeng Yuting Liu Chen Jin 

机构地区：[1]The Sixty-Third Research Institute,National University of Defense Technology,Nanjing,210007,China [2]Hunan Normal University,Changsha,410000,China [3]School of Computer&Software,Nanjing University of Information Science and Technology,Nanjing,210044,China [4]School of Computer Science,University of Manchester,Manchester,M139PL,UK

出　　处：《Computers, Materials & Continua》2023年第1期641-654,共14页计算机、材料和连续体（英文）

基　　金：supported by the Hunan Provincial Natural Science Foundation of China(Grant No.2020JJ4624);the National Social Science Fund of China(Grant No.20&ZD047);the Scientific Research Fund of Hunan Provincial Education Department(Grant No.19A020);the National University of Defense Technology Research Project ZK20-46 and the Young Elite Scientists Sponsorship Program 2021-JCJQ-QT-050.

摘　　要：Event detection(ED)is aimed at detecting event occurrences and categorizing them.This task has been previously solved via recognition and classification of event triggers(ETs),which are defined as the phrase or word most clearly expressing event occurrence.Thus,current approaches require both annotated triggers as well as event types in training data.Nevertheless,triggers are non-essential in ED,and it is time-wasting for annotators to identify the“most clearly”word from a sentence,particularly in longer sentences.To decrease manual effort,we evaluate event detectionwithout triggers.We propose a novel framework that combines Type-aware Attention and Graph Convolutional Networks(TA-GCN)for event detection.Specifically,the task is identified as a multi-label classification problem.We first encode the input sentence using a novel type-aware neural network with attention mechanisms.Then,a Graph Convolutional Networks(GCN)-based multilabel classification model is exploited for event detection.Experimental results demonstrate the effectiveness.

关 键 词：Event detection information extraction type-aware attention graph convolutional networks 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]