CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data
Suman Dowlagar, Radhika Mamidi · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
Identifying named entities is, in general, a practical and challenging task in the field of Natural Language Processing.Named Entity Recognition on the code-mixed text is further challenging due to the linguistic complexity resulting from the nature of the mixing.This paper addresses the submission of team CM-NEROne to the SEMEVAL 2022 shared task 11 MultiCoNER.The Code-mixed NER task aimed to identify named entities on the codemixed dataset.Our work consists of Named Entity Recognition (NER) on the code-mixed dataset by leveraging the multilingual data.We achieved a weighted average F1 score of 0.7044, i.e., 6% greater than the baseline.