Team UMBC-FEVER : Claim verification using Semantic Lexical Resources

Ankur Padia, Francis Ferraro, Tim Finin


Abstract
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.
Anthology ID:
W18-5527
Volume:
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2018
Address:
Brussels, Belgium
Editors:
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
161–165
Language:
URL:
https://aclanthology.org/W18-5527
DOI:
10.18653/v1/W18-5527
Bibkey:
Cite (ACL):
Ankur Padia, Francis Ferraro, and Tim Finin. 2018. Team UMBC-FEVER : Claim verification using Semantic Lexical Resources. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 161–165, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Team UMBC-FEVER : Claim verification using Semantic Lexical Resources (Padia et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5527.pdf
Data
FrameNet