Abstract
In this paper, we present IIITBH team’s effort to solve the second shared task of the 6th Workshop on Noisy User-generated Text (W-NUT)i.e Identification of informative COVID-19 English Tweets. The central theme of the task is to develop a system that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is Informative or not. Our approach is based on exploiting semantic information from both max pooling and average pooling, to this end we propose two models.- Anthology ID:
- 2020.wnut-1.46
- Volume:
- Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 342–346
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.46
- DOI:
- 10.18653/v1/2020.wnut-1.46
- Cite (ACL):
- Saichethan Reddy and Pradeep Biswal. 2020. IIITBH at WNUT-2020 Task 2: Exploiting the best of both worlds. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 342–346, Online. Association for Computational Linguistics.
- Cite (Informal):
- IIITBH at WNUT-2020 Task 2: Exploiting the best of both worlds (Reddy & Biswal, WNUT 2020)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2020.wnut-1.46.pdf