Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition
Atharvan Dogra, Prabsimran Kaur, Guneet Singh Kohli, Jatin Bedi · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
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 information extraction, question answering, and machine translation.This paper describes our participating system run to the Named entity recognition and classification shared task SemEval-2022.The task is motivated towards detecting semantically ambiguous and complex entities in short and 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.