Abstract
This paper describes our systems in social media mining for health applications (SMM4H) shared task. We participated in all four tracks of the shared task using linear models with a combination of character and word n-gram features. We did not use any external data or domain specific information. The resulting systems achieved above-average scores among other participating systems, with F1-scores of 91.22, 46.8, 42.4, and 85.53 on tasks 1, 2, 3, and 4 respectively.- Anthology ID:
- W18-5914
- Volume:
- Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher, Abeed Sarker, Michael Paul
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 52–53
- Language:
- URL:
- https://aclanthology.org/W18-5914
- DOI:
- 10.18653/v1/W18-5914
- Cite (ACL):
- Çağrı Çöltekin and Taraka Rama. 2018. Drug-Use Identification from Tweets with Word and Character N-Grams. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 52–53, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Drug-Use Identification from Tweets with Word and Character N-Grams (Çöltekin & Rama, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W18-5914.pdf