mlsanityData to 14 Oct 2026

CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data

arXiv 2206.07318 ↗First posted 15 Jun 2022cs.CLcited by 4

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Abstract (arXiv)

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 CMNEROne to the SEMEVAL 2022 shared task 11 MultiCoNER. The Code-mixed NER task aimed to identify named entities on the code-mixed 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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 15 Jun 2022.

Builds on

6 of 21 references.

References · 6

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
  4. 2020A New Dataset for Natural Language Inference from Code-mixed Conversationscited by 29
  5. 2019Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Taskcited by 24
  6. 2014Targetable Named Entity Recognition in Social Mediacited by 8

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 3.

4 papers.

  1. 2022The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challengescited by 35
  2. 2025COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixingcited by 7
  3. 2025Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalitiescited by 5
  4. 2025Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognitioncited by 0