Abstract
This paper describes the approach of team Kit Kittredge to SemEval-2019 Task 4: Hyperpartisan News Detection. The goal was binary classification of news articles into the categories of “biased” or “unbiased”. We had two software submissions: one a simple bag-of-words model, and the second an LSTM (Long Short Term Memory) neural network, which was trained on a subset of the original dataset selected by a voting system of other LSTMs. This method did not prove much more successful than the baseline, however, due to the models’ tendency to learn publisher-specific traits instead of general bias.- Anthology ID:
- S19-2178
- 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:
- 1021–1025
- Language:
- URL:
- https://aclanthology.org/S19-2178
- DOI:
- 10.18653/v1/S19-2178
- Cite (ACL):
- Rebekah Cramerus and Tatjana Scheffler. 2019. Team Kit Kittredge at SemEval-2019 Task 4: LSTM Voting System. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1021–1025, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Team Kit Kittredge at SemEval-2019 Task 4: LSTM Voting System (Cramerus & Scheffler, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S19-2178.pdf