Name
Title | ||
|---|---|---|
Emory at WNUT-2020 Task 2 - Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification. |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.18653/v1/2020.wnut-1.54 | W-NUT@EMNLP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuting Guo | 1 | 0 | 0.68 |
| Mohammed Ali Al-Garadi | 2 | 2 | 2.41 |
| Abeed Sarker | 3 | 60 | 7.38 |