@inproceedings{misra-boye-2024-nested,
    title = "Nested Noun Phrase Identification Using {BERT}",
    author = "Misra, Shweta  and
      Boye, Johan",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1062",
    pages = "12138--12143",
    abstract = "For several NLP tasks, an important substep is the identification of noun phrases in running text. This has typically been done by {``}chunking{''} {--} a way of finding minimal noun phrases by token classification. However, chunking-like methods do not represent the fact that noun phrases can be nested. This paper presents a novel method of finding all noun phrases in a sentence, nested to an arbitrary depth, using the BERT model for token classification. We show that our proposed method achieves very good results for both Swedish and English.",
}

For several NLP tasks, an important substep is the identification of noun phrases in running text. This has typically been done by {``}chunking{''} {--} a way of finding minimal noun phrases by token classification. However, chunking-like methods do not represent the fact that noun phrases can be nested. This paper presents a novel method of finding all noun phrases in a sentence, nested to an arbitrary depth, using the BERT model for token classification. We show that our proposed method achieves very good results for both Swedish and English.
Misra, Shweta Boye, Johan
Nested Noun Phrase Identification Using BERT