A Majority Voting Strategy of a SciBERT-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task) - Laboratoire Interdisciplinaire des Sciences du Numérique Access content directly
Conference Papers Year : 2022

A Majority Voting Strategy of a SciBERT-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task)

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 32 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.
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Dates and versions

hal-04425922 , version 1 (30-01-2024)

Identifiers

  • HAL Id : hal-04425922 , version 1

Cite

Atilla Kaan Alkan, Cyril Grouin, Fabian Schüssler, Pierre Zweigenbaum. A Majority Voting Strategy of a SciBERT-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task). First Workshop on Information Extraction from Scientific Publications, Association for Computational Linguistics, Nov 2022, Online, Taiwan. pp.131-139. ⟨hal-04425922⟩
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