@inproceedings{stevanoski-gievska-2019-team,
title = "Team {N}ed {L}eeds at {S}em{E}val-2019 Task 4: Exploring Language Indicators of Hyperpartisan Reporting",
author = "Stevanoski, Bozhidar and
Gievska, Sonja",
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/S19-2179",
doi = "10.18653/v1/S19-2179",
pages = "1026--1031",
abstract = "This paper reports an experiment carried out to investigate the relevance of several syntactic, stylistic and pragmatic features on the task of distinguishing between mainstream and partisan news articles. The results of the evaluation of different feature sets and the extent to which various feature categories could affect the performance metrics are discussed and compared. Among different combinations of features and classifiers, Random Forest classifier using vector representations of the headline and the text of the report, with the inclusion of 8 readability scores and few stylistic features yielded best result, ranking our team at the 9th place at the SemEval 2019 Hyperpartisan News Detection challenge.",
}
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<abstract>This paper reports an experiment carried out to investigate the relevance of several syntactic, stylistic and pragmatic features on the task of distinguishing between mainstream and partisan news articles. The results of the evaluation of different feature sets and the extent to which various feature categories could affect the performance metrics are discussed and compared. Among different combinations of features and classifiers, Random Forest classifier using vector representations of the headline and the text of the report, with the inclusion of 8 readability scores and few stylistic features yielded best result, ranking our team at the 9th place at the SemEval 2019 Hyperpartisan News Detection challenge.</abstract>
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%0 Conference Proceedings
%T Team Ned Leeds at SemEval-2019 Task 4: Exploring Language Indicators of Hyperpartisan Reporting
%A Stevanoski, Bozhidar
%A Gievska, Sonja
%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 stevanoski-gievska-2019-team
%X This paper reports an experiment carried out to investigate the relevance of several syntactic, stylistic and pragmatic features on the task of distinguishing between mainstream and partisan news articles. The results of the evaluation of different feature sets and the extent to which various feature categories could affect the performance metrics are discussed and compared. Among different combinations of features and classifiers, Random Forest classifier using vector representations of the headline and the text of the report, with the inclusion of 8 readability scores and few stylistic features yielded best result, ranking our team at the 9th place at the SemEval 2019 Hyperpartisan News Detection challenge.
%R 10.18653/v1/S19-2179
%U https://aclanthology.org/S19-2179
%U https://doi.org/10.18653/v1/S19-2179
%P 1026-1031
Markdown (Informal)
[Team Ned Leeds at SemEval-2019 Task 4: Exploring Language Indicators of Hyperpartisan Reporting](https://aclanthology.org/S19-2179) (Stevanoski & Gievska, SemEval 2019)
ACL