@inproceedings{chatterjee-etal-2023-lsjsp,
title = "{LSJSP} at {S}em{E}val-2023 Task 2: {FTBC}: A {F}ast{T}ext based framework with pre-trained {BERT} for {NER}",
author = "Chatterjee, Shilpa and
Evenss, Leo and
Bhattacharyya, Pramit and
Mondal, Joydeep",
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-1.174",
doi = "10.18653/v1/2023.semeval-1.174",
pages = "1254--1259",
abstract = "This study introduces the system submitted to the SemEval 2022 Task 2: MultiCoNER II (Multilingual Complex Named Entity Recognition) by the LSJSP team. We propose FTBC, a FastText-based framework with pre-trained Bert for NER tasks with complex entities and over a noisy dataset. Our system achieves an average of 58.27{\%} F1 score (fine-grained) and 75.79{\%} F1 score (coarse-grained) across all languages. FTBC outperforms the baseline BERT-CRF model on all 12 monolingual tracks.",
}
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<abstract>This study introduces the system submitted to the SemEval 2022 Task 2: MultiCoNER II (Multilingual Complex Named Entity Recognition) by the LSJSP team. We propose FTBC, a FastText-based framework with pre-trained Bert for NER tasks with complex entities and over a noisy dataset. Our system achieves an average of 58.27% F1 score (fine-grained) and 75.79% F1 score (coarse-grained) across all languages. FTBC outperforms the baseline BERT-CRF model on all 12 monolingual tracks.</abstract>
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%0 Conference Proceedings
%T LSJSP at SemEval-2023 Task 2: FTBC: A FastText based framework with pre-trained BERT for NER
%A Chatterjee, Shilpa
%A Evenss, Leo
%A Bhattacharyya, Pramit
%A Mondal, Joydeep
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%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 chatterjee-etal-2023-lsjsp
%X This study introduces the system submitted to the SemEval 2022 Task 2: MultiCoNER II (Multilingual Complex Named Entity Recognition) by the LSJSP team. We propose FTBC, a FastText-based framework with pre-trained Bert for NER tasks with complex entities and over a noisy dataset. Our system achieves an average of 58.27% F1 score (fine-grained) and 75.79% F1 score (coarse-grained) across all languages. FTBC outperforms the baseline BERT-CRF model on all 12 monolingual tracks.
%R 10.18653/v1/2023.semeval-1.174
%U https://aclanthology.org/2023.semeval-1.174
%U https://doi.org/10.18653/v1/2023.semeval-1.174
%P 1254-1259
Markdown (Informal)
[LSJSP at SemEval-2023 Task 2: FTBC: A FastText based framework with pre-trained BERT for NER](https://aclanthology.org/2023.semeval-1.174) (Chatterjee et al., SemEval 2023)
ACL