ES4ED: An Event Detection Model Based on Event Sentence Pre-Determination
As an important task in the field of information extraction, event detection is widely used in event graph construction and network public opinion monitoring. Although the existing methods (such as BGCN, MGRN-EE, etc.) have obtained well performance on event detection by utilizing various features f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10477654/ |
| _version_ | 1797231239089881088 |
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| author | Jizhao Zhu Haonan Zhao Wenyu Duan Xinlong Pan Chunlong Fan |
| author_facet | Jizhao Zhu Haonan Zhao Wenyu Duan Xinlong Pan Chunlong Fan |
| author_sort | Jizhao Zhu |
| collection | DOAJ |
| description | As an important task in the field of information extraction, event detection is widely used in event graph construction and network public opinion monitoring. Although the existing methods (such as BGCN, MGRN-EE, etc.) have obtained well performance on event detection by utilizing various features from text, they neglect that the events in data follows a long-tailed distribution, which leads to a serious bias in the trained event detection model. By following a simple but effective way to address this issue, we propose an event detection model based on event sentence pre-determination, termed as ES4ED. The model first employs classification method to identify the sentences that contain events semantically (called event sentences), and then conducts event detection on these event sentences to solve the long-tailed distribution of events. ES4ED consists of three components: the semantic encoder, the event sentence decider and the event detector. First, the semantic encoder encodes the words semantically. Then, the event sentence decider identifies event sentences by classification. Finally, the event sentences are input to the event detector to complete the event triggers identification and classification. Experimental results on the public dataset ACE2005 show that the F1 score of the proposed model achieves 79.2% and 76.5% on trigger identification and trigger classification, respectively, which are significantly improved compared with the existing typical works. |
| first_indexed | 2024-04-24T15:41:13Z |
| format | Article |
| id | doaj.art-f3e9b22e020f4b1798af70a7c00a0d21 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T15:41:13Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f3e9b22e020f4b1798af70a7c00a0d212024-04-01T23:00:42ZengIEEEIEEE Access2169-35362024-01-0112453594536810.1109/ACCESS.2024.338041510477654ES4ED: An Event Detection Model Based on Event Sentence Pre-DeterminationJizhao Zhu0https://orcid.org/0009-0006-0106-0170Haonan Zhao1https://orcid.org/0009-0007-3764-7982Wenyu Duan2https://orcid.org/0009-0000-6933-4728Xinlong Pan3Chunlong Fan4School of Computer Science, Shenyang Aerospace University, Shenyang, ChinaSchool of Computer Science, Shenyang Aerospace University, Shenyang, ChinaSchool of Computer Science, Shenyang Aerospace University, Shenyang, ChinaInstitute of Information Fusion, Naval Aeronautical University, Yantai, ChinaSchool of Computer Science, Shenyang Aerospace University, Shenyang, ChinaAs an important task in the field of information extraction, event detection is widely used in event graph construction and network public opinion monitoring. Although the existing methods (such as BGCN, MGRN-EE, etc.) have obtained well performance on event detection by utilizing various features from text, they neglect that the events in data follows a long-tailed distribution, which leads to a serious bias in the trained event detection model. By following a simple but effective way to address this issue, we propose an event detection model based on event sentence pre-determination, termed as ES4ED. The model first employs classification method to identify the sentences that contain events semantically (called event sentences), and then conducts event detection on these event sentences to solve the long-tailed distribution of events. ES4ED consists of three components: the semantic encoder, the event sentence decider and the event detector. First, the semantic encoder encodes the words semantically. Then, the event sentence decider identifies event sentences by classification. Finally, the event sentences are input to the event detector to complete the event triggers identification and classification. Experimental results on the public dataset ACE2005 show that the F1 score of the proposed model achieves 79.2% and 76.5% on trigger identification and trigger classification, respectively, which are significantly improved compared with the existing typical works.https://ieeexplore.ieee.org/document/10477654/Event extractionevent detectionpre-determinationevent sentence |
| spellingShingle | Jizhao Zhu Haonan Zhao Wenyu Duan Xinlong Pan Chunlong Fan ES4ED: An Event Detection Model Based on Event Sentence Pre-Determination IEEE Access Event extraction event detection pre-determination event sentence |
| title | ES4ED: An Event Detection Model Based on Event Sentence Pre-Determination |
| title_full | ES4ED: An Event Detection Model Based on Event Sentence Pre-Determination |
| title_fullStr | ES4ED: An Event Detection Model Based on Event Sentence Pre-Determination |
| title_full_unstemmed | ES4ED: An Event Detection Model Based on Event Sentence Pre-Determination |
| title_short | ES4ED: An Event Detection Model Based on Event Sentence Pre-Determination |
| title_sort | es4ed an event detection model based on event sentence pre determination |
| topic | Event extraction event detection pre-determination event sentence |
| url | https://ieeexplore.ieee.org/document/10477654/ |
| work_keys_str_mv | AT jizhaozhu es4edaneventdetectionmodelbasedoneventsentencepredetermination AT haonanzhao es4edaneventdetectionmodelbasedoneventsentencepredetermination AT wenyuduan es4edaneventdetectionmodelbasedoneventsentencepredetermination AT xinlongpan es4edaneventdetectionmodelbasedoneventsentencepredetermination AT chunlongfan es4edaneventdetectionmodelbasedoneventsentencepredetermination |