Perez Almendros, Carla  ORCID: https://orcid.org/0000-0001-9360-4011, Espinosa-Anke, Luis ORCID: https://orcid.org/0000-0001-6830-9176 and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881
2019.
Cardiff University at SemEval-2019 Task 4: Linguistic features for hyperpartisan news detection.
Presented at: SemEval-2019: International Workshop on Semantic Evaluation,
Minneapolis, Minnesota, USA,
6-7 June 2019.
Association for Computational Linguistics,
pp. 929-933.
10.18653/v1/S19-2158
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Official URL: https://doi.org/10.18653/v1/S19-2158
Abstract
This paper summarizes our contribution to the Hyperpartisan News Detection task in SemEval 2019. We experiment with two different approaches: 1) an SVM classifier based on word vector averages and hand-crafted linguistic features, and 2) a BiLSTM-based neural text classifier trained on a filtered training set. Surprisingly, despite their different nature, both approaches achieve an accuracy of 0.74. The main focus of this paper is to further analyze the remarkable fact that a simple feature-based approach can perform on par with modern neural classifiers. We also highlight the effectiveness of our filtering strategy for training the neural network on a large but noisy training set.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Association for Computational Linguistics |
| ISBN: | 9781950737062 |
| Related URLs: | |
| Date of First Compliant Deposit: | 15 May 2019 |
| Last Modified: | 27 Feb 2025 14:53 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/122142 |
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