@inproceedings{srivastava-etal-2019-vernon,
title = "Vernon-fenwick at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features",
author = "Srivastava, Vertika and
Gupta, Ankita and
Prakash, Divya and
Sahoo, Sudeep Kumar and
R.R, Rohit and
Kim, Yeon Hyang",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2189/",
doi = "10.18653/v1/S19-2189",
pages = "1078--1082",
abstract = "In this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (onesided). It shows blind beliefs, opinions and unreasonable adherence to a party, idea, faction or a person. Through this task, we aim to develop an automated system that can be used to detect hyperpartisan news and serve as a prescreening technique for fake news detection. The proposed system jointly uses a rich set of handcrafted textual and semantic features. Our system achieved 2nd rank on the primary metric (82.0{\%} accuracy) and 1st rank on the secondary metric (82.1{\%} F1-score), among all participating teams. Comparison with the best performing system on the leaderboard shows that our system is behind by only 0.2{\%} absolute difference in accuracy."
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<abstract>In this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (onesided). It shows blind beliefs, opinions and unreasonable adherence to a party, idea, faction or a person. Through this task, we aim to develop an automated system that can be used to detect hyperpartisan news and serve as a prescreening technique for fake news detection. The proposed system jointly uses a rich set of handcrafted textual and semantic features. Our system achieved 2nd rank on the primary metric (82.0% accuracy) and 1st rank on the secondary metric (82.1% F1-score), among all participating teams. Comparison with the best performing system on the leaderboard shows that our system is behind by only 0.2% absolute difference in accuracy.</abstract>
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%0 Conference Proceedings
%T Vernon-fenwick at SemEval-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features
%A Srivastava, Vertika
%A Gupta, Ankita
%A Prakash, Divya
%A Sahoo, Sudeep Kumar
%A R.R, Rohit
%A Kim, Yeon Hyang
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F srivastava-etal-2019-vernon
%X In this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (onesided). It shows blind beliefs, opinions and unreasonable adherence to a party, idea, faction or a person. Through this task, we aim to develop an automated system that can be used to detect hyperpartisan news and serve as a prescreening technique for fake news detection. The proposed system jointly uses a rich set of handcrafted textual and semantic features. Our system achieved 2nd rank on the primary metric (82.0% accuracy) and 1st rank on the secondary metric (82.1% F1-score), among all participating teams. Comparison with the best performing system on the leaderboard shows that our system is behind by only 0.2% absolute difference in accuracy.
%R 10.18653/v1/S19-2189
%U https://aclanthology.org/S19-2189/
%U https://doi.org/10.18653/v1/S19-2189
%P 1078-1082
Markdown (Informal)
[Vernon-fenwick at SemEval-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features](https://aclanthology.org/S19-2189/) (Srivastava et al., SemEval 2019)
ACL