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Team UMBC-FEVER : Claim verification using Semantic Lexical Resources
- Source :
- Proceedings of the First Workshop on Fact Extraction and VERification (FEVER).
- Publication Year :
- 2018
- Publisher :
- Association for Computational Linguistics, 2018.
-
Abstract
- Proceedings of the First Workshop on Fact Extraction and Verification<br />We describe our system used in the 2018 FEVER shared task. The system employed a frame-based information retrieval approach to select Wikipedia sentences providing evidence and used a two-layer multilayer perceptron to classify a claim as correct or not. Our submission achieved a score of 0.3966 on the Evidence F1 metric with accuracy of 44.79%, and FEVER score of 0.2628 F1 points.
- Subjects :
- Claim
fact verification
Computer science
business.industry
Frame (networking)
computer.software_genre
Task (project management)
Multilayer perceptron
Metric (mathematics)
UMBC Ebiquity Research Group
Artificial intelligence
natural language processing
business
Semantic Web
computer
Lexical Resources
Natural language processing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
- Accession number :
- edsair.doi.dedup.....00b37b5b84679c664bdd677a5333aff7
- Full Text :
- https://doi.org/10.18653/v1/w18-5527