Proceedings of the First Workshop on Fact Extraction and Verification

Team UMBC-FEVER: Claim verification using Semantic Lexical Resources

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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 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 an accuracy of 44.79% and a FEVER score of 0.2628 F1 points.

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fact verification, frames, knowledge graph, natural language processing, natural language processing


Association for Computational Linguistics

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