@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T MLlab4CS at SemEval-2023 Task 2: Named Entity Recognition in Low-resource Language Bangla Using Multilingual Language Models
%A Mukherjee, Shrimon
%A Ghosh, Madhusudan
%A Basuchowdhuri, Partha
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%A Girish
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F mukherjee-etal-2023-mllab4cs
%X 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.
%R 10.18653/v1/2023.semeval-1.192
%U https://aclanthology.org/2023.semeval-1.192
%U https://doi.org/10.18653/v1/2023.semeval-1.192
%P 1388-1394
Markdown (Informal)
[MLlab4CS at SemEval-2023 Task 2: Named Entity Recognition in Low-resource Language Bangla Using Multilingual Language Models](https://aclanthology.org/2023.semeval-1.192) (Mukherjee et al., SemEval 2023)
ACL