Abstract
This paper describes the system proposed by Sabancı University Natural Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an unsupervised entity linking pipeline that detects potential entity mentions with the help of Wikipedia and also uses the corresponding Wikipedia context to help the classifier in finding the named entity type of that mention. The proposed pipeline significantly improved the performance, especially for complex entities in low-context settings.- Anthology ID:
- 2022.semeval-1.227
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1648–1653
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.227
- DOI:
- 10.18653/v1/2022.semeval-1.227
- Cite (ACL):
- Buse Çarık, Fatih Beyhan, and Reyyan Yeniterzi. 2022. SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1648–1653, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking (Çarık et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.227.pdf