@inproceedings{sengupta-pedersen-2019-duluth,
title = "{D}uluth at {S}em{E}val-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector",
author = "Sengupta, Saptarshi and
Pedersen, Ted",
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/S19-2162",
doi = "10.18653/v1/S19-2162",
pages = "949--953",
abstract = "This paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4. Hyperpartisan news is highly polarized and takes a very biased or one{--}sided view of a particular story. We developed two variants of our system, the more successful was a Logistic Regression classifier based on unigram features. This was our official entry in the task, and it placed 23rd of 42 participating teams. Our second variant was a Convolutional Neural Network that did not perform as well.",
}
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<abstract>This paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4. Hyperpartisan news is highly polarized and takes a very biased or one–sided view of a particular story. We developed two variants of our system, the more successful was a Logistic Regression classifier based on unigram features. This was our official entry in the task, and it placed 23rd of 42 participating teams. Our second variant was a Convolutional Neural Network that did not perform as well.</abstract>
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%0 Conference Proceedings
%T Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector
%A Sengupta, Saptarshi
%A Pedersen, Ted
%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 sengupta-pedersen-2019-duluth
%X This paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4. Hyperpartisan news is highly polarized and takes a very biased or one–sided view of a particular story. We developed two variants of our system, the more successful was a Logistic Regression classifier based on unigram features. This was our official entry in the task, and it placed 23rd of 42 participating teams. Our second variant was a Convolutional Neural Network that did not perform as well.
%R 10.18653/v1/S19-2162
%U https://aclanthology.org/S19-2162
%U https://doi.org/10.18653/v1/S19-2162
%P 949-953
Markdown (Informal)
[Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector](https://aclanthology.org/S19-2162) (Sengupta & Pedersen, SemEval 2019)
ACL