DEERE: Document-Level Event Extraction as Relation Extraction Article Swipe
Jian Li
,
Ruijuan Hu
,
Keliang Zhang
,
Haiyan Liu
,
Yanzhou Ma
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/2742796
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/2742796
The descriptions of complex events usually span sentences, so we need to extract complete event information from the whole document. To address the challenges of document-level event extraction, we propose a novel framework named Document-level Event Extraction as Relation Extraction (DEERE), which is suitable for document-level event extraction tasks without trigger-word labelling. By well-designed task transformation, DEERE remodels event extraction as single-stage relation extraction, which can mitigate error propagation. A long text supported encoder is adopted in the relation extraction model to aware the global context effectively. A fault-tolerant event integration algorithm is designed to improve the prediction accuracy. Experimental results show that our approach advances the SOTA for the ChFinAnn dataset by an average F1-score of 3.7. The code and data are available at https://github.com/maomaotfntfn/DEERE.
Related Topics
Concepts
Computer science
Event (particle physics)
Relationship extraction
Context (archaeology)
Relation (database)
Task (project management)
Transformation (genetics)
Extraction (chemistry)
Information extraction
Natural language processing
Data mining
Encoder
Artificial intelligence
Code (set theory)
Word (group theory)
Information retrieval
Programming language
Linguistics
Paleontology
Operating system
Quantum mechanics
Biology
Chromatography
Gene
Set (abstract data type)
Management
Physics
Chemistry
Biochemistry
Economics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/2742796
- https://downloads.hindawi.com/journals/misy/2022/2742796.pdf
- OA Status
- hybrid
- Cited By
- 4
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293468105
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4293468105Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2022/2742796Digital Object Identifier
- Title
-
DEERE: Document-Level Event Extraction as Relation ExtractionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-26Full publication date if available
- Authors
-
Jian Li, Ruijuan Hu, Keliang Zhang, Haiyan Liu, Yanzhou MaList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/2742796Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/misy/2022/2742796.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/misy/2022/2742796.pdfDirect OA link when available
- Concepts
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Computer science, Event (particle physics), Relationship extraction, Context (archaeology), Relation (database), Task (project management), Transformation (genetics), Extraction (chemistry), Information extraction, Natural language processing, Data mining, Encoder, Artificial intelligence, Code (set theory), Word (group theory), Information retrieval, Programming language, Linguistics, Paleontology, Operating system, Quantum mechanics, Biology, Chromatography, Gene, Set (abstract data type), Management, Physics, Chemistry, Biochemistry, Economics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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