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.
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Interdisciplinary Bridge
— Artificial Intelligence and Natural Language Processing
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Trend Setter
— Knowledge
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Keyword Pioneer
— semantic lexical resource
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Hot Topic Early Bird
— claim verification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio