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

arXiv: https://arxiv.org/abs/2206.07318 · HTML: https://mlsanity.com/paper/2206.07318/

## 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 (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [A New Dataset for Natural Language Inference from Code-mixed Conversations](https://mlsanity.com/paper/2004.05051/) (2020)
- [Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task](https://mlsanity.com/paper/1906.04138/) (2019)
- [Targetable Named Entity Recognition in Social Media](https://mlsanity.com/paper/1408.0782/) (2014)

## Built on by (most cited first, 4 of 4)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing](https://mlsanity.com/paper/2503.21670/) (2025)
- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition](https://mlsanity.com/paper/2509.02514/) (2025)

## 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.