@inproceedings{padia-etal-2018-team,
    title = "Team {UMBC}-{FEVER} : Claim verification using Semantic Lexical Resources",
    author = "Padia, Ankur  and
      Ferraro, Francis  and
      Finin, Tim",
    editor = "Thorne, James  and
      Vlachos, Andreas  and
      Cocarascu, Oana  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-5527/",
    doi = "10.18653/v1/W18-5527",
    pages = "161--165",
    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."
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    <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.</abstract>
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%0 Conference Proceedings
%T Team UMBC-FEVER : Claim verification using Semantic Lexical Resources
%A Padia, Ankur
%A Ferraro, Francis
%A Finin, Tim
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F padia-etal-2018-team
%X 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.
%R 10.18653/v1/W18-5527
%U https://aclanthology.org/W18-5527/
%U https://doi.org/10.18653/v1/W18-5527
%P 161-165
Markdown (Informal)
[Team UMBC-FEVER : Claim verification using Semantic Lexical Resources](https://aclanthology.org/W18-5527/) (Padia et al., EMNLP 2018)
ACL