COVCOR20 at WNUT-2020 Task 2: An Attempt to Combine Deep Learning and Expert rules

Ali Hürriyetoğlu, Ali Safaya, Osman Mutlu, Nelleke Oostdijk, Erdem Yörük


Abstract
In the scope of WNUT-2020 Task 2, we developed various text classification systems, using deep learning models and one using linguistically informed rules. While both of the deep learning systems outperformed the system using the linguistically informed rules, we found that through the integration of (the output of) the three systems a better performance could be achieved than the standalone performance of each approach in a cross-validation setting. However, on the test data the performance of the integration was slightly lower than our best performing deep learning model. These results hardly indicate any progress in line of integrating machine learning and expert rules driven systems. We expect that the release of the annotation manuals and gold labels of the test data after this workshop will shed light on these perplexing results.
Anthology ID:
2020.wnut-1.75
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
495–498
Language:
URL:
https://aclanthology.org/2020.wnut-1.75
DOI:
10.18653/v1/2020.wnut-1.75
Bibkey:
Cite (ACL):
Ali Hürriyetoğlu, Ali Safaya, Osman Mutlu, Nelleke Oostdijk, and Erdem Yörük. 2020. COVCOR20 at WNUT-2020 Task 2: An Attempt to Combine Deep Learning and Expert rules. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 495–498, Online. Association for Computational Linguistics.
Cite (Informal):
COVCOR20 at WNUT-2020 Task 2: An Attempt to Combine Deep Learning and Expert rules (Hürriyetoğlu et al., WNUT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.wnut-1.75.pdf
Data
WNUT-2020 Task 2