Capturing semantic features to improve Chinese event detection  

作　　者：Xiaobo Ma Yongbin Liu Chunping Ouyang 

机构地区：[1]School of Computing,University of South China,Hengyang,China 2Hunan provincial base for scientific and technological innovation cooperation,Hunan,China

出　　处：《CAAI Transactions on Intelligence Technology》2022年第2期219-227,共9页智能技术学报（英文）

基　　金：973 Program,Grant/Award Number:2014CB340504;The State Key Program of National Natural Science of China,Grant/Award Number:61533018;National Natural Science Foundation of China,Grant/Award Number:61402220;The Philosophy and Social Science Foundation of Hunan Province,Grant/Award Number:16YBA323;Natural Science Foundation of Hunan Province,Grant/Award Number:2020JJ4525;Scientific Research Fund of Hunan Provincial Education Department,Grant/Award Number:18B279,19A439。

摘　　要：Current Chinese event detection methods commonly use word embedding to capture semantic representation,but these methods find it difficult to capture the dependence relationship between the trigger words and other words in the same sentence.Based on the simple evaluation,it is known that a dependency parser can effectively capture dependency relationships and improve the accuracy of event categorisation.This study proposes a novel architecture that models a hybrid representation to summarise semantic and structural information from both characters and words.This model can capture rich semantic features for the event detection task by incorporating the semantic representation generated from the dependency parser.The authors evaluate different models on kbp 2017 corpus.The experimental results show that the proposed method can significantly improve performance in Chinese event detection.

关 键 词：dependency parser event detection hybrid representation learning semantic feature 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]