Abstract
This paper describes the system we built as the YNU-HPCC team in the SemEval-2021 Task 11: NLPContributionGraph. This task involves first identifying sentences in the given natural language processing (NLP) scholarly articles that reflect research contributions through binary classification; then identifying the core scientific terms and their relation phrases from these contribution sentences by sequence labeling; and finally, these scientific terms and relation phrases are categorized, identified, and organized into subject-predicate-object triples to form a knowledge graph with the help of multiclass classification and multi-label classification. We developed a system for this task using a pre-trained language representation model called BERT that stands for Bidirectional Encoder Representations from Transformers, and achieved good results. The average F1-score for Evaluation Phase 2, Part 1 was 0.4562 and ranked 7th, and the average F1-score for Evaluation Phase 2, Part 2 was 0.6541, and also ranked 7th.- Anthology ID:
- 2021.semeval-1.58
- 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:
- 478–484
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.58
- DOI:
- 10.18653/v1/2021.semeval-1.58
- Cite (ACL):
- Xinge Ma, Jin Wang, and Xuejie Zhang. 2021. YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly Articles. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 478–484, Online. Association for Computational Linguistics.
- Cite (Informal):
- YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly Articles (Ma et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.58.pdf