Abstract
Named Entity Recognition (NER), an essential subtask in NLP that identifies text belonging to predefined semantics such as a person, location, organization, drug, time, clinical procedure, biological protein, etc. NER plays a vital role in various fields such as informationextraction, question answering, and machine translation. This paper describes our participating system run to the Named entity recognitionand classification shared task SemEval-2022. The task is motivated towards detecting semantically ambiguous and complex entities in shortand low-context settings. Our team focused on improving entity recognition by improving the word embeddings. We concatenated the word representations from State-of-the-art language models and passed them to find the best representation through a reinforcement trainer. Our results highlight the improvements achieved by various embedding concatenations.- Anthology ID:
- 2022.semeval-1.217
- 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:
- 1576–1582
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.217
- DOI:
- 10.18653/v1/2022.semeval-1.217
- Cite (ACL):
- Atharvan Dogra, Prabsimran Kaur, Guneet Kohli, and Jatin Bedi. 2022. Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1576–1582, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition (Dogra et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.217.pdf
- Data
- MultiCoNER