@inproceedings{mukherjee-etal-2023-mllab4cs,
    title = "{ML}lab4{CS} at {S}em{E}val-2023 Task 2: Named Entity Recognition in Low-resource Language {B}angla Using Multilingual Language Models",
    author = "Mukherjee, Shrimon  and
      Ghosh, Madhusudan  and
      {Girish}  and
      Basuchowdhuri, Partha",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.192",
    doi = "10.18653/v1/2023.semeval-1.192",
    pages = "1388--1394",
    abstract = "Extracting of NERs from low-resource languages and recognizing their types is one of the important tasks in the entity extraction domain. Recently many studies have been conducted in this area of research. In our study, we introduce a system for identifying complex entities and recognizing their types from low-resource language Bangla, which was published in SemEval Task 2 MulitCoNER II 2023. For this sequence labeling task, we use a pre-trained language model built on a natural language processing framework. Our team name in this competition is MLlab4CS. Our model Muril produces a macro average F-score of $76.27\%$, which is a comparable result for this competition.",
}

Extracting of NERs from low-resource languages and recognizing their types is one of the important tasks in the entity extraction domain. Recently many studies have been conducted in this area of research. In our study, we introduce a system for identifying complex entities and recognizing their types from low-resource language Bangla, which was published in SemEval Task 2 MulitCoNER II 2023. For this sequence labeling task, we use a pre-trained language model built on a natural language processing framework. Our team name in this competition is MLlab4CS. Our model Muril produces a macro average F-score of $76.27\%$, which is a comparable result for this competition.
Mukherjee, Shrimon Ghosh, Madhusudan {Girish} Basuchowdhuri, Partha
MLlab4CS at SemEval-2023 Task 2: Named Entity Recognition in Low-resource Language Bangla Using Multilingual Language Models