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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 12/2023

27.06.2023 | Original Article

General fine-grained event detection based on fusion of multi-information representation and attention mechanism

verfasst von: Xinyu He, Ge Yan, Changfu Si, Yonggong Ren

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 12/2023

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Abstract

Event extraction is an important field in information extraction, which aims to extract key information from unstructured text automatically. Event extraction is mainly divided into trigger identification and classification. The existing models are deficient in sentence representation in the initial word embeddings training process, which makes it difficult to capture the deep bidirectional representation and can’t handle the semantic information of the context well, thus affecting the performance of event detection. In this paper, a model BMRMC (BERT + Mean pooling layer + Relative position in multi-head attention + CRF) based on multi-information representation and attention mechanism is proposed. Firstly, the BERT pre-training model based on a bidirectional training transformer is used to embed words and extract word-level features. Then the sentence-level semantic representation is fused by mean pooling layer. In addition, relative position is combined with multi-head attention, which can strengthen the connection of contents. Finally, the sequences are labeled by CRF based on the BIO-labeling mechanism. The experimental results show that the proposed model BMRMC improves the performance of event detection, and the F value on the MAVEN dataset is 67.74%, which achieves state-of-the-art performance in the general fine-grained event detection task.
Vorheriger Artikel Robust stability of complex-valued fractional-order neural networks with uncertain parameters based on new integral inequalities
Nächster Artikel Convergence analysis for sparse Pi-sigma neural network model with entropy error function

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Literatur
1.
Wei X, Bang W (2019) A survey of event extraction from text. IEEE Access 99:1–1
2.
Wei H, Ai Z et al (2021) Biomedical event trigger extraction based on multi-layer residual BiLSTM and contextualized word representations. Int J Mach Learn Cybern 12(18):1–13
3.
Wan QZ, Wan CX, Hu R et al (2021) Chinese financial event extraction based on syntactic and semantic dependency parsing. Chin J Comput 44(3):508–530
4.
Ribeiro S, Olivier F, and Xavier T (2017) Unsupervised event clustering and aggregation from newswire and web articles. In: Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pp 62–67
5.
Yu SQ, Bin W (2018) Exploiting structured news information to improve event detection via dual-level clustering. In: IEEE Third International Conference on Data Science in Cyberspace, pp 873–880
6.
Björne J (2014) Biomedical event extraction with machine learning. University of Turku, Turku
7.
Yu H, Wen Z et al. (2018) Self-regulation: employing a generative adversarial network to improve event detection. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, volume 1: Long Papers, Association for Computational Linguistics, pp 515–526
8.
Nie YF, Rong WG et al (2015) Embedding assisted prediction architecture for event trigger identification. J Bioinform Comput Biol 13(3):1540001CrossRef
9.
Liu SL, Chen YB et al. (2017) Exploiting argument information to improve event detection via supervised attention mechanisms. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, volume 1: Long Papers, Association for Computational Linguistics, pp 1789–1798
10.
Liu SL, Chen YB et al. (2016) Leveraging FrameNet to improve automatic event detection. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, volume 1: Long Papers, The Association for Computer Linguistics, pp 98–108
11.
Zhan LY, Jiang XP, Liu Q (2021) Research on Chinese event extraction method based on HMM and multi-stage method. J Phys: Conf Ser 1732(1):1–4
12.
Collins M, Singer Y (1999) Unsupervised models for named entity classification. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp 100–110
13.
Kim JT, Moldovan DI (1995) Acquisition of linguistic patterns for knowledge-based information extraction. IEEE Trans Knowl Data Eng 7(5):713–724CrossRef
14.
Li Q, Ji H, Huang L (2013) Joint event extraction via structured prediction with global features. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, volume 1: Long Papers, pp 73–82
15.
Ahn D (2006) The stages of event extraction. In: Proceedings of the Workshop on Annotating and Reasoning about Time and Events. MIT Press, Cambridge, pp 1–8
16.
Saha S, Majumder A et al. (2011) Bio-molecular event extraction using support vector machine. In: Proceedings of the 3rd International Conference on Advanced Computing, Piscataway: IEEE, pp 298–303
17.
Nguyen TH, Grishman R (2015) Event detection and domain adaptation with convolutional neural network. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Stroudsburg, ACL, pp 365–371
18.
Liu J, Chen Y et al. (2018) Event detection via gated multilingual attention mechanism. In: AAAI Conference on Artificial Intelligence; Innovative Applications of Artificial Intelligence Conference; Symposium on Educational Advances in Artificial Intelligence in Proc. 32nd AAAI Conf, pp 4865–4872
19.
Tong M H, Bin X et al. (2020) Improving event detection via open-domain trigger knowledge. In: Association for Computational Linguistics in ACL, pp 5887–5897
20.
Xiang LA, Mc B et al (2021) A hybrid medical text classification framework: integrating attentive rule construction and neural network. Neurocomputing 443:345–355CrossRef
21.
Xu R X, Liu T Y, Li L, et al. (2021) Document-level event extraction via heterogeneous graph-based interaction model with a tracker. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp 3533–3546
22.
23.
Sheng J W, Guo S, Yu B W, et al. (2021) CasEE: a joint learning framework with cascade decoding for overlapping event extraction. In: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp 164–174
24.
Vaswani A, Shazeer N et al. (2017) Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp 6000–6010
25.
Peters M E, Neumann M et al. (2018) Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics. New Orleans, pp 2227
26.
Radford A, Karthik N et al. (2018) Improving language understanding by generative pre-training. Technical report, OpenAI, CA
27.
Devlin J, Chang M W et al. (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
28.
Chen Y B, Yang H, Liu K, et al. (2018) Collective event detection via a hierarchical and bias tagging networks with gated multi-level attention mechanisms. In: Proceedings of EMNLP: Conference on Empirical Methods in Natural Language Processing, pp d18–1158
29.
Lou D F, Liao Z L, Deng S M et al. (2021) MLBiNet: a cross-sentence collective event detection network. In: Proceedings of ACL: Annual Meeting of the Association for Computational Linguistics, acl-long.373
30.
31.
Wang X Z, Zi Q W et al. (2020) MAVEN: a massive general domain event detection dataset. In: EMNLP: Online, Association for Computational Linguistics, pp 1652–1671
32.
Pyysalo S, Ohta T, Miwa M et al (2012) Event extraction across multiple levels of biological organization. Bioinformatics 28(18):i575–i581CrossRef
33.
Chen Y B, Li H X et al. (2015) Event extraction via dynamic multi-pooling convolutional neural networks. In: ACL-IJCNLP, as sociation for Computational Linguistics, pp 167–176
34.
Hochreiter S, Jürgen S (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRef
35.
John L, McCallum A et al. (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. Department of Computer & Information Science, pp 6–28
36.
Yan H R, Xiao L J et al. (2019) Event detection with multi-order graph convolution and aggregated attention. In: EMNLP, Association for Computational Linguistics, pp 5765–5769
37.
Wang X Z, Xu H et al. (2019) Adversarial training for weakly supervised event detection. In: NAACL, Minneapolis, Minnesota. Association for Computational Linguistics, pp 998–718 1008
38.
Zhou D, Zhong D (2015) A semi-supervised learning framework for biomedical event extraction based on hidden topics. Artif Intell Med 64(1):51–58CrossRef
39.
Wang J, Zhang J, Yuan A, et al. (2015) Biomedical event trigger detection by dependency-based word embedding. In: Proceeding of IEEE International Conference on Bioinformatics and Biomedicine. IEEE, pp 429–432
40.
Hai LT, Thy TT, Duong K et al (2020) DeepEventMine: end-to-end neural nested event extraction from biomedical texts. Bioinformatics 36(19):4910–4917CrossRef
41.
Chen Y (2019) Multiple-level biomedical event trigger recognition with transfer learning. BMC Bioinform 20:459CrossRef
Metadaten
Titel
General fine-grained event detection based on fusion of multi-information representation and attention mechanism
verfasst von
Xinyu He
Ge Yan
Changfu Si
Yonggong Ren
Publikationsdatum
27.06.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 12/2023
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01900-y

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