Team UMBC-FEVER: Claim verification using Semantic Lexical Resources

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W18-5527.pdf (848.86 KB)

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https://www.aclweb.org/anthology/W18-5527/

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http://hdl.handle.net/11603/11967
 http://dx.doi.org/10.18653/v1/W18-5527

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

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Date

2018-11-01

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conference papers and proceedings
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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Attribution 4.0 International

Subjects

Claim
Semantic Web
fact verification
natural language processing
Lexical Resources
UMBC Ebiquity Research Group
UMBC Ebiquity Research Group

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.