JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (7): 53-63.doi: 10.6040/j.issn.1671-9352.1.2023.080

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A document-level event extraction method based on core arguments

Chengjie SUN(),Zongwei LI,Lili SHAN,Lei LIN   

  1. Faculty of Computing, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
  • Received:2023-10-18 Online:2024-07-20 Published:2024-07-15

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Abstract:

A document-level event extraction method based on core arguments(CA-DocEE) is proposed, which defines criteria for selecting core arguments based on their distributions in document-level events, uses heterogeneous graph convolutional neural networks to augment document contextual information for encoding argument entities, and captures deep semantic information in sentences based on machine reading comprehension methods for classifying the role of arguments. On the document-level event extraction dataset, the method proposed in this paper achieves a micro-average F1 value of 80.1%, which is comparable with the state-of-the-art methods.

Key words: event extraction, document-level event extraction, machine reading comprehension, graph convolutional neural network

CLC Number: 

  • TP391.1

Fig.1

Architecture of discourse level event extraction model based on argument relationship graph"

Fig.2

Example of argument relationship diagram"

Fig.3

Diagram of argument classification model"

Table 1

Comparison of the results of argument mention 单位: %"

论元提及识别模型 micro-P micro-R micro-F1
BiLSTM+CRF 88.0 82.9 85.4
BERT+MCRF 91.0 91.2 91.1

Table 2

Comparison of overall experimental results 单位: %"

模型 S M All
DCFEE-O 72.4 52.4 63.2
GIT 86.8 72.3 79.9
PTPCG 88.2 69.1 79.4
PTPCG复现结果 86.2 68.6 78.5
CA-DocEE 88.0 70.3 80.1

Table 3

Results of ablation experiment 单位: %"

模型 micro-P micro-R micro-F1
使用Zhu[1]的伪触发词作为核心论元 82.7 76.8 79.6
-MRC 83.2 76.9 79.9
CA-DocEE 83.7 77.4 80.1
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