Abstract
This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: “Identifi- cation of Informative COVID-19 English Tweet”. Our system explores three recent Transformer- based deep learning models pretrained on large- scale data to encode documents. Moreover, we developed two feature enrichment methods to en- hance document embeddings by integrating emoji embeddings and syntactic features into deep learn- ing models. Our system achieved F1-score of 0.897 and accuracy of 90.1% on the test set, and ranked in the top-third of all 55 teams.- Anthology ID:
- 2020.wnut-1.54
- 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:
- 388–393
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.54
- DOI:
- 10.18653/v1/2020.wnut-1.54
- Cite (ACL):
- Yuting Guo, Mohammed Ali Al-Garadi, and Abeed Sarker. 2020. Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 388–393, Online. Association for Computational Linguistics.
- Cite (Informal):
- Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification (Guo et al., WNUT 2020)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2020.wnut-1.54.pdf
- Data
- WNUT-2020 Task 2