Chinese News Event Corpus Construction Method Based on Syntax Tree
Pages 20 - 25
Abstract
At present, the weakly supervised model is usually used for the expansion of the event corpus, which avoids the expensive manual annotation process. However, the weakly supervised model relies on the knowledge base and a small part of manually annotated corpus data, which makes the model have the problems of poor portability. In order to solve this problem, we construct a public domain event extraction model using syntax tree. In this paper, we propose a classification structure of Chinese syntax tree according to the view of event extraction, and put forward an event extraction algorithm for various syntax tree types. Moreover, in the construction algorithm of trigger word dictionary, we use cross-corpus dictionary information to construct Chinese trigger word dictionary from the perspective of semantics. As a result, we obtain 40,128 Chinese news events, which initially constituted the corpus of Chinese new events.
References
[1]
Chen, Zheng, and Heng Ji. "Language specific issue and feature exploration in Chinese event extraction." Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers. 2009.
[2]
Yang, Hang, et al. "Dcfee: A document-level chinese financial event extraction system based on automatically labeled training data." Proceedings of ACL 2018, System Demonstrations. 2018.
[3]
Rao, Sudha, et al. "Biomedical event extraction using abstract meaning representation." BioNLP 2017. 2017.
[4]
Zeng, Ying, et al. "Scale up event extraction learning via automatic training data generation. " Thirty-Second AAAI Conference on Artificial Intelligence. 2018.
[5]
Li, Wei, et al. "Joint event extraction based on hierarchical event schemas from framenet." IEEE Access 7 (2019): 25001--25015.
[6]
Liu, Shulin, et al. "Leveraging framenet to improve automatic event detection." (2016).
[7]
Chen, Yubo, et al. "Automatically labeled data generation for large scale event extraction." Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.
[8]
Araki, Jun, and Teruko Mitamura. "Open-domain event detection using distant supervision." Proceedings of the 27th International Conference on Computational Linguistics. 2018.
[9]
Abney, Steven. "Bootstrapping." Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 2002.
[10]
Liao, Shasha, and Ralph Grishman. "Can document selection help semi-supervised learning: a case study on event extraction." Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2. Association for Computational Linguistics, 2011.
[11]
Ferguson, James, et al. "Semi-supervised event extraction with paraphrase clusters." arXiv preprint arXiv:1808.08622 (2018).
[12]
Wang, Xiaozhi, et al. "Adversarial training for weakly supervised event detection." Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
[13]
Gerner, Deborah J., et al. "Conflict and mediation event observations (CAMEO): A new event data framework for the analysis of foreign policy interactions." International Studies Association, New Orleans (2002).
[14]
Azar, Edward E. "The conflict and peace data bank (COPDAB) project." Journal of Conflict Resolution 24.1 (1980): 143--152.
[15]
Leetaru, Kalev, and Philip A. Schrodt. "Gdelt: Global data on events, location, and tone, 1979-2012." ISA annual convention. Vol. 2. No. 4. Citeseer, 2013.
[16]
Osorio, Javier, et al. "Translating CAMEO verbs for automated coding of event data." International Interactions 45.6 (2019): 1049--1064.
[17]
Manning, Christopher D., et al. "The Stanford CoreNLP natural language processing toolkit." Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. 2014.
[18]
Li, Jingyang, and Maosong Sun. "Scalable term selection for text categorization." Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). 2007.
[19]
Norris, Clayton, Philip Schrodt, and John Beieler. "PETRARCH2: Another event coding program." Journal of Open Source Software 2.9 (2017): 133.
Index Terms
- Chinese News Event Corpus Construction Method Based on Syntax Tree
Recommendations
An event-extraction approach for business analysis from online Chinese news
Highlights- This paper presented a business event-extraction approach from online Chinese news.
AbstractExtracting events from business news aids users to perceive market trends, be aware of competitors’ strategies, and to make valuable investment decisions. Prior research lacks event extraction in the area of business and event based ...
Towards Event Timeline Generation from Vietnamese News
Computational Linguistics and Intelligent Text ProcessingAbstractEvent timeline generation is an active research area in many languages. In this paper, we describes our attempts towards event timeline generation from Vietnamese newswire documents based on the work in [1]. Our main contributions are: i) To our ...
Enhancing Chinese Event Extraction with Event Trigger Structures
The dependency syntactic structure is widely used in event extraction. However, the dependency structure reflecting syntactic features is essentially different from the event structure that reflects semantic features, leading to the performance ...
Comments
Information & Contributors
Information
Published In
September 2020
250 pages
ISBN:9781450387859
DOI:10.1145/3422713
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 October 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICBDT 2020
ICBDT 2020: 2020 3rd International Conference on Big Data Technologies
September 18 - 20, 2020
Qingdao, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 41Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 08 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in