@inproceedings{choudhary-etal-2023-iitd,
    title = "{IITD} at {S}em{E}val-2023 Task 2: A Multi-Stage Information Retrieval Approach for Fine-Grained Named Entity Recognition",
    author = "Choudhary, Shivani  and
      Chatterjee, Niladri  and
      Saha, Subir",
    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-1.111",
    doi = "10.18653/v1/2023.semeval-1.111",
    pages = "800--806",
    abstract = "MultiCoNER-II is a fine-grained Named Entity Recognition (NER) task that aims to identify ambiguous and complex named entities in multiple languages, with a small amount of contextual information available. To address this task, we propose a multi-stage information retrieval (IR) pipeline that improves the performance of language models for fine-grained NER. Our approach involves leveraging a combination of a BM25-based IR model and a language model to retrieve relevant passages from a corpus. These passages are then used to train a model that utilizes a weighted average of losses. The prediction is generated by a decoder stack that includes a projection layer and conditional random field. To demonstrate the effectiveness of our approach, we participated in the English track of the MultiCoNER-II competition. Our approach yielded promising results, which we validated through detailed analysis.",
}

MultiCoNER-II is a fine-grained Named Entity Recognition (NER) task that aims to identify ambiguous and complex named entities in multiple languages, with a small amount of contextual information available. To address this task, we propose a multi-stage information retrieval (IR) pipeline that improves the performance of language models for fine-grained NER. Our approach involves leveraging a combination of a BM25-based IR model and a language model to retrieve relevant passages from a corpus. These passages are then used to train a model that utilizes a weighted average of losses. The prediction is generated by a decoder stack that includes a projection layer and conditional random field. To demonstrate the effectiveness of our approach, we participated in the English track of the MultiCoNER-II competition. Our approach yielded promising results, which we validated through detailed analysis.
Choudhary, Shivani Chatterjee, Niladri Saha, Subir
IITD at SemEval-2023 Task 2: A Multi-Stage Information Retrieval Approach for Fine-Grained Named Entity Recognition