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2018 EMNLP EMNLP 2018

Team UMBC-FEVER : Claim verification using Semantic Lexical Resources

Abstract

AbstractWe 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.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing
📈 Trend Setter - Knowledge
🐣 Hot Topic Early Bird - multi-layer perceptron

Authors

Ankur Padia, Francis Ferraro, Tim Finin

Topics

Natural Language Processing > Applications > Fact-Checking Natural Language Processing > Applications > Information Extraction Natural Language Processing > Applications > Information Retrieval Artificial Intelligence > Core AI > Knowledge

Keywords

natural language inference claim verification information retrieval semantic analysis multi-layer perceptron evidence retrieval semantic lexical resource frame-based retrieval
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