Fermi at SemEval-2019 Task 4: The sarah-jane-smith Hyperpartisan News Detector
Nikhil Chakravartula, Vijayasaradhi Indurthi, Bakhtiyar Syed
Abstract
This paper describes our system (Fermi) for Task 4: Hyper-partisan News detection of SemEval-2019. We use simple text classification algorithms by transforming the input features to a reduced feature set. We aim to find the right number of features useful for efficient classification and explore multiple training models to evaluate the performance of these text classification algorithms. Our team - Fermi’s model achieved an accuracy of 59.10% and an F1 score of 69.5% on the official test data set. In this paper, we provide a detailed description of the approach as well as the results obtained in the task.- Anthology ID:
- S19-2163
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 954–956
- Language:
- URL:
- https://aclanthology.org/S19-2163
- DOI:
- 10.18653/v1/S19-2163
- Cite (ACL):
- Nikhil Chakravartula, Vijayasaradhi Indurthi, and Bakhtiyar Syed. 2019. Fermi at SemEval-2019 Task 4: The sarah-jane-smith Hyperpartisan News Detector. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 954–956, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Fermi at SemEval-2019 Task 4: The sarah-jane-smith Hyperpartisan News Detector (Chakravartula et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S19-2163.pdf