@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.",
}

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.
Chatterjee, Shilpa Evenss, Leo Bhattacharyya, Pramit Mondal, Joydeep
LSJSP at SemEval-2023 Task 2: FTBC: A FastText based framework with pre-trained BERT for NER