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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (12): 64-74    DOI: 10.11925/infotech.2096-3467.2022.0549
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Event Detection Model Based on Semantic Information Fusion
Wei Jianxiang1(),Lu Qian2,Han Pu1,Huang Weidong1,3
1School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
3Key Research Base of Philosophy and Social Sciences in Jiangsu-Information Industry Integration Innovation and Emergency Management Research Center, Nanjing 210003, China
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Abstract  

[Objective] This paper aims to improve the accuracy of event detection tasks by fusing semantic information. [Methods] First, we stored the non-relational semantic information with an initial word vector and encoded them with the Bi-LSTM model to aggregate their contexts. Then, we developed a relation graph based on relational semantic information. Third, we used a multi-scale convolutional neural network to capture the spatial information from the adjacency matrix and fuse it with the word vector. Finally, we built a Gate-GCN model to aggregate relational semantic information between adjacent word vectors to enhance their representation ability. [Results] We examined the new model with the ACE05 benchmark dataset. Our method’s F1 value reached 76.3%, which was 1.2% higher than the existing mainstream models. [Limitations] The proposed model needs to be validated with general datasets. [Conclusions] Fusion of multiple types of semantic information can effectively improve the event detection performance.
Key words： Event Detection      Information Extraction      Multi-Semantic Fusion      Gated Linear Unit      Graph Convolutional Neural Network     
Received: 30 May 2022      Published: 30 March 2023
ZTFLH:  TP391  
  G35  
Fund:National Social Science Fund of China(17CTQ022);National Natural Science Foundation of China(7227011403);Major Project of Philosophy and Social Science Research of Jiangsu Universities(2020SJZDA102)
Corresponding Authors: Wei Jianxiang，ORCID：0000-0001-9052-9212，E-mail：jxwei@njupt.edu.cn。   
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Jianxiang Wei
Qian Lu
Pu Han
Weidong Huang

Cite this article:

Wei Jianxiang, Lu Qian, Han Pu, Huang Weidong. Event Detection Model Based on Semantic Information Fusion. Data Analysis and Knowledge Discovery, 2023, 7(12): 64-74.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0549     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I12/64

S1：North Korea’s military may have fired a loser at a U.S helicopter.
Words
North Korea’s military
U.S		 Entity types
PER：Group
GPE：Nation
Words
North Korea’s military
Loser
helicopter		 Event arguments
Attacker
Instrument
Target
Trigger：fired
Event type：Attack
Event Detection Example Sentences Containing Multiple Types of Semantic Information
Event Detection Model Based on Multi-Semantic Information Fusion
Relationship Graph and Adjacency Matrix
超参数名 参数值 超参数名 参数值
初始词向量维度 100 批量大小 30
事件要素向量维度 100 最大句子长度 50
实体类型向量维度 25 Bi-LSTM层数 1
关系类型向量维度 50 GCN层数 2
Bi-LSTM隐藏层维度 100 学习率 0.1
GCN隐藏层维度 200 偏置项参数α 5
Hyperparameter Settings of the Model
网络层 FLOPs Params
CNN 9.02MMac 22.55k
Bi-LSTM 6.07MMac 301.6k
Gated-GCN 11.6MMac 52.75k
Model Complexity Analysis
模型 P/% R/% F1/%
Cross-entity 72.9 64.3 68.3
MaxEntropy 74.5 59.1 65.9
PSL 75.3 64.4 69.4
DMCNN 75.6 63.6 69.1
JRNN 66.0 73.0 69.3
ANN 78.0 66.3 71.7
GCN-ED 77.9 68.8 73.1
JMEE 76.3 71.3 73.7
SELF 71.3 74.7 73.0
BGCN
AD-DMBERT		 77.5
77.9		 72.4
72.5		 74.2
75.1
本文 76.0 76.7 76.3
Comparison of Model Performance
特征组合 P/% R/% F1/%
词向量表示： [ w ]
关系表示： [ d e p _ r ]		 69.7 70.6 70.1
词向量表示： [ w ; ? s ]
关系表示： [ d e p _ r ]		 71.1 74.1 72.5
词向量表示： [ w ; ? s ; ? a ]
关系表示： [ d e p _ r ]		 76.6 73.5 75.0
Performance Comparison of Different Feature Combinations
模型 P/% R/% F1/%
Bi-LSTM+GCN 76.6 73.5 75.0
CNN+Bi-LSTM+GCN 77.0 74.3 75.6
Model Performance for Fusing Spatial Information
模型 P/% R/% F1/%
CNN+Bi-LSTM+GCN 77.0 74.3 75.6
CNN+Bi-LSTM+Gated-GCN 76.0 76.7 76.3
Model Performance by Using Gated-GCN
Confusion Matrix Based on 8 Categories
Confusion Matrix Based on 34 Subclass Classification （Partial）
Confusion Matrix of Test Results of Benchmark Model
Confusion Matrix of the Test Results of the Proposed Model
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