针对事件描述边界模糊、句法语义信息利用率低的问题，提出了基于句法语义增强的实体事件关系对联合抽取模型。该方法充分考虑了实体和事件的上下文信息、时序信息和句法结构信息。首先，利用双向长短时记忆网络捕获文本的上下文信息和时序信息，生成语义更加丰富的嵌入表示；然后，设计了基于句法语义增强的实体事件关系对联合抽取模型，充分考虑文本的句法标签语义信息；最后，将实体事件关系对抽取和实体事件匹配两个子任务进行关联，实现实体事件关系对的联合抽取。实验结果表明，所提出的联合抽取方法在提高模型训练效率的同时，可以实现模型训练参数共享并减少错误标签传播，有效提高了实体事件关系对抽取的准确率。
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.
[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.
