Journal of East China Normal University(Natural Science) >

2023 , Vol. 2023 >Issue 2: 60 - 72

DOI: https://doi.org/10.3969/j.issn.1000-5641.2023.02.008

Computer Science

Target-dependent event detection from news

  • Tiantian ZHANG ,
  • Man LAN
Expand
  • School of Computer Science and Technology, East China Normal University, Shanghai　200062, China

Received date: 2021-09-28

  Online published: 2023-03-23

Fold

Abstract

In real-world scenarios, various events in the news are not only too nuanced and complex to distinguish, but also involve multiple entities. To address these problems, previous event-centric methods are designed to detect events first and then extract arguments, relying on imperfect performance for event trigger detection; this process, however, is unfit to deal with the sheer volume of news in the real world. Given that the performance of named entity recognition (NER) is satisfactory, we shift our perspective from an event-centric to a target-centric view. This paper proposes a new task: target-dependent event detection (TDED), which aims to extract target entities and detect their corresponding events. We also propose a semantic and syntactic aware approach to support thousands of target entity extractions first and subsequently the detection of dozens of event types; this approach can be applied to data from massive corporations. Experimental results on a real-world Chinese financial dataset demonstrated that our model outperformed previous methods, particularly in complex scenarios.

Key words： target-dependent; event detection; entity recognition; event keywords; syntactic dependency distance

Cite this article

Tiantian ZHANG , Man LAN . Target-dependent event detection from news[J]. Journal of East China Normal University(Natural Science), 2023 , 2023(2) : 60 -72 . DOI: 10.3969/j.issn.1000-5641.2023.02.008

References

1 YANG B S, MITCHELL T M. Joint extraction of events and entities within a document context [C]// Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics (ACL), 2016: 289-299. DOI: 10.18653/v1/N16-1.
2 WADDEN D, WENNBERG U, LUAN Y, et al. Entity, relation, and event extraction with contextualized span representations [C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics(ACL), 2019: 5784-5789. DOI: 10.18653/v1/D19-1585.
3 YANG S, FENG D W, QIAO L B, et al. Exploring pre-trained language models for event extraction and generation [C] // Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics (ACL), 2019: 5284-5294. DOI: 10.18653/v1/P19-1522.
4 MA J, WANG S, ANUBHAI R, et al. Resource-enhanced neural model for event argument extraction [C] // Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics(ACL), 2020: 3554-3559. DOI: 10.18653/v1/2020.findings-emnlp.318.
5 LIN Y, JI H, HUANG F, et al. A joint neural model for information extraction with global features [C] // Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics(ACL), 2020: 7999-8009. DOI: 10.18653/v1/2020.acl-main.713.
6 LINGUISTIC DATA CONSORTIUM. ACE [EB/OL]. (2021-03-10)[2021-06-23]. https://www.ldc.upenn.edu/collaborations/past-projects/ace.
7 ZHENG S, CAO W, XU W, et al. Doc2EDAG: An end-to-end document-level framework for Chinese financial event extraction [C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics (ACL), 2019: 337-346. DOI: 10.18653/v1/D19-1032.
8 LIANG X, CHENG D W, YANG F Z, et al. F-HMTC: Detecting financial events for investment decisions based on neural hierarchical multi-label text classification [C]// Proceedings of the 29th International Joint Conference on Artificial Intelligence(IJCAI-20). 2020: 4490-4496.
9 WANG X Z, WANG Z Q, HAN X, et al. MAVEN: A massive general domain event detection dataset [C] // Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics(ACL), 2020: 1652-1671.
10 LIU J, CHEN Y B, LIU K, et al. How does context matter? On the robustness of event detection with context-selective mask generalization [C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. Association for Computational Linguistics(ACL), 2020: 2523-2532.
11 DING N, LI Z R, LIU Z Y, et al. Event detection with trigger-aware lattice neural network [C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics (ACL), 2019: 347-356.
12 TONG M H, XU B, WANG S, et al. Improving event detection via open-domain trigger knowledge [C] // Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics (ACL), 2020: 5887-5897.
13 ZHANG Y Y, XU G L, WANG Y, et al. A question answering-based framework for one-step event argument extraction. IEEE Access, 2020, (8): 65420- 65431.
14 HU M H, PENG Y X, HUANG Z, et al. Open-domain targeted sentiment analysis via span-based extraction and classification [C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics (ACL), 2019: 537-546.
15 MAO Y, SHEN Y, YU C, et al. A joint training dual-MRC framework for aspect based sentiment analysis [C]// Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, 2021, 35(15): 13543-13551.
16 ZHONG Z X, CHEN D Q. A frustratingly easy approach for joint entity and relation extraction [EB/OL]. (2021-03-23)[2021-06-23]. https://arxiv.org/abs/2010.12812.
17 SOUZA F, NOGUEIRA R, LOTUFO R. Portuguese named entity recognition using BERT-CRF [EB/OL]. (2020-02-27)[2021-06-23]. https://arxiv.org/abs/1909.10649v2.
18 LIU M Y, TU Z Y, ZHONG T, et al. LTP: A new active learning strategy for bert-CRF based named entity recognition [EB/OL]. (2020-08-27)[2021-06-23]. https://arxiv.org/abs/2001.02524.
19 LIU Y H, OTT M, GOYAL N, et al. RoBERTa: A robustly optimized bert pretraining approach [EB/OL]. (2019-07-26)[2021-06-23]. https://arxiv.org/abs/1907.11692.
20 LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields: Probabilistic models for segmenting and labeling sequence data [C]// Proceedings of the 18th International Conference on Machine Learning (ICML-2001). San Francisco: Morgan Kaufmann Publishers Inc., 2001: 282-289.
21 FORNEY G D. The viterbi algorithm: A personal history [EB/OL]. (2005-04-29)[2021-06-23]. https://arxiv.org/pdf/cs/0504020.pdf.
22 GITHUB. WoBERT: Word-based Chinese BERT model-ZhuiyiAI [EB/OL]. [2021-06-23]. https://github.com/ZhuiyiTechnology/WoBERT.
23 ZENG B Q, YANG H, XU R Y, et al. LCF: A local context focus mechanism for aspect-based sentiment classification. Applied Sciences, 2019, 9 (16): 3389.
24 GITHUB. 2021百度关系抽取比赛Baseline [EB/OL]. (2021-03-10)[2021-06-23]. https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/information_extraction/DuIE.
25 SONG Y W, WANG J H, JIANG T, et al. Attentional encoder network for targeted sentiment classification [C]// Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series, ICANN 2019, Lecture Notes in Computer Science, vol 11730. Cham: Springer, 2019: 93-103.
26 LOSHCHILOV I, HUTTER F. Fixing weight decay regularization in Adam[EB/OL]. (2019-01-04)[2021-06-23]. https://arxiv.org/abs/1711.05101.
Options
Outlines

/

〈 〉