Abstract
This paper describes the Duluth system that participated in SemEval-2021 Task 11, NLP Contribution Graph. It details the extraction of contribution sentences and scientific entities and their relations from scholarly articles in the domain of Natural Language Processing. Our solution uses deBERTa for multi-class sentence classification to extract the contributing sentences and their type, and dependency parsing to outline each sentence and extract subject-predicate-object triples. Our system ranked fifth of seven for Phase 1: end-to-end pipeline, sixth of eight for Phase 2 Part 1: phrases and triples, and fifth of eight for Phase 2 Part 2: triples extraction.- Anthology ID:
- 2021.semeval-1.60
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 490–501
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.60
- DOI:
- 10.18653/v1/2021.semeval-1.60
- Cite (ACL):
- Anna Martin and Ted Pedersen. 2021. Duluth at SemEval-2021 Task 11: Applying DeBERTa to Contributing Sentence Selection and Dependency Parsing for Entity Extraction. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 490–501, Online. Association for Computational Linguistics.
- Cite (Informal):
- Duluth at SemEval-2021 Task 11: Applying DeBERTa to Contributing Sentence Selection and Dependency Parsing for Entity Extraction (Martin & Pedersen, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.semeval-1.60.pdf
- Data
- SemEval-2017 Task-10, SemEval-2021 Task-11