基于句法语义增强的实体事件关系对联合抽取

doi:10.3969/j.issn.0255-8297.2025.06.011

Abstract

Abstract: To address the issues of fuzzy event description boundaries and insufficient utilization of syntactic and semantic information, this paper proposes a syntactic-semantic-enhanced model for joint extraction of entity-event relationships. The proposed approach comprehensively incorporates contextual, temporal, and syntactic structural information of entities and events. Firstly, it utilizes bidirectional long short-term memory networks (BiLSTM) to capture the contextual and temporal information of the text, generating more semantically enriched embeddings. Secondly, a syntactic-semantic-enhanced model is designed for joint extraction of entity-event relationships, considering the syntactic label semantics in the text. Finally, it establishes a connection between the extraction of entity-event relationships and entity-event matching, achieving the joint extraction of entity-event relationships. Experimental results demonstrate that the proposed joint extraction method not only improves model training efficiency but also enables parameter sharing and reduces error label propagation, thereby enhancing the accuracy of entity-event relationship pair extraction.

Key words: event extraction, entity-event matching, event relation pair extraction, syntactic semantic enhancement, bidirectional long short-term memory network (BiLSTM), graph convolutional network (GCN)

CLC Number:

TP391

GAO Jianqi, HUANG Dian, LUO Xiangfeng. Entity-Event Relation Joint Extraction Enhanced with Syntactic Semantic[J]. Journal of Applied Sciences, 2025, 43(6): 1024-1036.

References

[1] Liao S, Grishman R. Using document level cross-event inference to improve event extraction [C]//48th Annual Meeting of the Association for Computational Linguistics, 2010: 789-797.
[2] Zhang J, He Q, Zhang Y. Syntax grounded graph convolutional network for joint entity and event extraction [J]. Neurocomputing, 2021, 422: 118-128.
[3] Yang H, Sui D, Chen Y, et al. Document-level event extraction via parallel prediction networks [C]//59th Annual Meeting of the Association for Computational Linguistics, 2021: 6298-6308
[4] Riloff E. Automatically constructing a dictionary for information extraction tasks [C]//Eleventh National Conference on Artificial Intelligence, 1993: 811-816.
[5] Riloff E, Shoen J. Automatically acquiring conceptual patterns without an annotated corpus [C]//Third Workshop on Very Large Corpora, 1995: 148-161.
[6] Yangarber R. Scenario customization for information extraction [D]. New York: New York University, 2001.
[7] 姜吉发. 自由文本的信息抽取模式获取的研究[D]. 北京: 中国科学院研究生院（计算技术研究所）, 2004.
[8] Ahn D. The stages of event extraction [C]//Workshop on Annotating and Reasoning about Time and Events, 2006: 1-8.
[9] Llorens H, Saquete E, Navarro-Colorado B. TimeML events recognition and classification: learning CRF models with semantic roles [C]//23rd International Conference on Computational Linguistics (Coling 2010), 2010: 725-733.
[10] Li P, Zhu Q, Zhou G. Joint modeling of argument identification and role determination in Chinese event extraction with discourse-level information [C]//Twenty-Third International Joint Conference on Artificial Intelligence, 2013: 2120-2126.
[11] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space [DB/OL]. (2013-01-16) [2023-09-28]. https://arxiv.org/abs/1301.3781.
[12] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality [DB/OL]. (2013-10-16) [2023-09-28]. https://arxiv.org/abs/1310.4546.
[13] Zhu L, Zheng H. Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks [J]. BMC Bioinformatics, 2020, 21(1): 1-12.
[14] Caselli T, Mutlu O, Basile A, et al. Protest-er: retraining bert for protest event extraction [C]//4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), 2021: 12-19.
[15] Chen Y, Xu L, Liu K, et al. Event extraction via dynamic multi-pooling convolutional neural networks [C]//53rd Annual Meeting of the Association for Computational Linguistics, 2015: 167-176.
[16] Nguyen T H, Cho K, Grishman R. Joint event extraction via recurrent neural networks [C]//2016 Conference of the North American Chapter of the Association for Computational Linguistics, 2016: 300-309.
[17] Sha L, Qian F, Chang B, et al. Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction [C]//Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, 2018: 5916-5923.
[18] Liu X, Luo Z, Huang H. Jointly multiple events extraction via attention-based graph information aggregation [C]//2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, 2018: 1247-1256.
[19] Veyseh A P B, Nguyen T N, Nguyen T H. Graph transformer networks with syntactic and semantic structures for event argument extraction [DB/OL]. (2020-10-26) [2023-09-28]. https://arxiv.org/abs/2010.13391.
[20] Feng L, Qiao L, Han Y, et al. Syntactic enhanced projection network for few-shot chinese event extraction [C]//International Conference on Knowledge Science, Engineering and Management, 2021: 75-87.
[21] Wang S, Yu M, Chang S, et al. Query and extract: refining event extraction as type-oriented binary decoding [DB/OL]. (2021-10-14) [2023-09-28]. https://arxiv.org/abs/2110.07476.
[22] Cui S Y, Yu B, Liu T W, et al. Edge-enhanced graph convolution networks for event detection with syntactic relation [DB/OL]. (2020-02-25) [2023-09-28]. https://arxiv.org/abs/2002.10757.
[23] Chen T, Xu R, He Y, et al. Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN [J]. Expert Systems with Applications: An International Journal, 2017, 72(C): 221-230.
[24] Luo L, Yang Z, Yang P, et al. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition [J]. Bioinformatics (Oxford, England), 2018, 34(8): 1381- 1388.
[25] Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding [DB/OL]. (2018-10-11) [2023-09-28]. https://arxiv.org/abs/1810.04805.
[26] Marcheggiani D, Titov I. Encoding sentences with graph convolutional networks for semantic role labeling [DB/OL]. (2017-03-14) [2023-09-28]. https://arxiv.org/abs/1703.04826.
[27] Che W, Li Z, Liu T. LTP: a chinese language technology platform [C]//Coling 2010: Demonstrations, 2010: 13-16.
[28] Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering [DB/OL]. (2016-06-30) [2023-09-28]. https://arxiv.org/abs/1606.09375.
[29] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks [DB/OL]. (2017-02-06) [2023-09-28]. https://arxiv.org/abs/1609.02907.
[30] Nguyen T H, Grishman R. Graph convolutional networks with argument-aware pooling for event detection [C]//Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, 2018: 5900-5907.
[31] Strubell E, Verga P, Belanger D, et al. Fast and accurate entity recognition with iterated dilated convolutions [DB/OL]. (2017-07-22) [2023-09-28]. https://arxiv.org/abs/1702.02098.
[32] Kim Y. Convolutional neural networks for sentence classification [C]//2014 Conference on Empirical Methods in Natural Language Processing. Doha, Qatar. Association for Computational Linguistics, 2014: 1746-1751.
[33] Lai S, Xu L, Liu K, et al. Recurrent convolutional neural networks for text classification [C]//Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015: 2267-2273.
[34] Zhou P, Shi W, Tian J, et al. Attention-based bidirectional long short-term memory networks for relation classification [C]//54th Annual Meeting of the Association for Computational Linguistics, 2016: 207-212.

Entity-Event Relation Joint Extraction Enhanced with Syntactic Semantic

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles

Metrics

Comments

[1]	LU Jing, GE Cong. Point of Interest Recommendation Method Combining Universal Trajectory Maps and Multiple Preferences [J]. Journal of Applied Sciences, 2025, 43(5): 771-784.
[2]	ZHAO Yuhao, CHEN Yanping, HUANG Ruizhang, QING Yongbin. Chinese Event Trigger Extraction Based on Span Regression [J]. Journal of Applied Sciences, 2023, 41(1): 95-106.