Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Data

Akash Gangwar, Sabhay Jain, Shubham Sourav, Ashutosh Modi


Abstract
This paper presents the system for SemEval 2021 Task 8 (MeasEval). MeasEval is a novel span extraction, classification, and relation extraction task focused on finding quantities, attributes of these quantities, and additional information, including the related measured entities, properties, and measurement contexts. Our submitted system, which placed fifth (team rank) on the leaderboard, consisted of SciBERT with [CLS] token embedding and CRF layer on top. We were also placed first in Quantity (tied) and Unit subtasks, second in MeasuredEntity, Modifier and Qualifies subtasks, and third in Qualifier subtask.
Anthology ID:
2021.semeval-1.175
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1232–1238
Language:
URL:
https://aclanthology.org/2021.semeval-1.175
DOI:
10.18653/v1/2021.semeval-1.175
Bibkey:
Cite (ACL):
Akash Gangwar, Sabhay Jain, Shubham Sourav, and Ashutosh Modi. 2021. Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Data. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 1232–1238, Online. Association for Computational Linguistics.
Cite (Informal):
Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Data (Gangwar et al., SemEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.175.pdf
Code
 akashgnr31/Counts-And-Measurement