Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features

Rodrigo Agerri


Abstract
In this paper we describe our participation to the Hyperpartisan News Detection shared task at SemEval 2019. Motivated by the late arrival of Doris Martin, we test a previously developed document classification system which consists of a combination of clustering features implemented on top of some simple shallow local features. We show how leveraging distributional features obtained from large in-domain unlabeled data helps to easily and quickly develop a reasonably good performing system for detecting hyperpartisan news. The system and models generated for this task are publicly available.
Anthology ID:
S19-2161
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
944–948
Language:
URL:
https://aclanthology.org/S19-2161
DOI:
10.18653/v1/S19-2161
Bibkey:
Cite (ACL):
Rodrigo Agerri. 2019. Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 944–948, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features (Agerri, SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2161.pdf