Abstract
This paper presents our submissions to SemEval-2019 Task9, Suggestion Mining. Our system is one in a series of systems in which we compare an approach using expert-defined rules with a comparable one using machine learning. We target tasks with a syntactic or semantic component that might be better described by a human understanding the task than by a machine learner only able to count features. For Semeval-2019 Task 9, the expert rules clearly outperformed our machine learning model when training and testing on equally balanced testsets.- Anthology ID:
- S19-2219
- 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:
- 1247–1253
- Language:
- URL:
- https://aclanthology.org/S19-2219
- DOI:
- 10.18653/v1/S19-2219
- Cite (ACL):
- Nelleke Oostdijk and Hans van Halteren. 2019. Team Taurus at SemEval-2019 Task 9: Expert-informed pattern recognition for suggestion mining. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1247–1253, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- Team Taurus at SemEval-2019 Task 9: Expert-informed pattern recognition for suggestion mining (Oostdijk & van Halteren, SemEval 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S19-2219.pdf