A Majority Voting Strategy of a SciBERT-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task)
Atilla Kaan Alkan, Cyril Grouin, Fabian Schussler, Pierre Zweigenbaum
Abstract
Detecting Entities in the Astrophysics Literature (DEAL) is a proposed shared task in the scope of the first Workshop on Information Extraction from Scientific Publications (WIESP) at AACL-IJCNLP 2022. It aims to propose systems identifying astrophysical named entities. This article presents our system based on a majority voting strategy of an ensemble composed of multiple SciBERT models. The system we propose is ranked second and outperforms the baseline provided by the organisers by achieving an F1 score of 0.7993 and a Matthews Correlation Coefficient (MCC) score of 0.8978 in the testing phase.- Anthology ID:
- 2022.wiesp-1.17
- Volume:
- Proceedings of the first Workshop on Information Extraction from Scientific Publications
- Month:
- November
- Year:
- 2022
- Address:
- Online
- Venue:
- WIESP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 145–150
- Language:
- URL:
- https://aclanthology.org/2022.wiesp-1.17
- DOI:
- Cite (ACL):
- Atilla Kaan Alkan, Cyril Grouin, Fabian Schussler, and Pierre Zweigenbaum. 2022. A Majority Voting Strategy of a SciBERT-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task). In Proceedings of the first Workshop on Information Extraction from Scientific Publications, pages 145–150, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Majority Voting Strategy of a SciBERT-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task) (Alkan et al., WIESP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.wiesp-1.17.pdf