COLING 2024main0 citations
Nested Noun Phrase Identification Using BERT
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.
BibTeX
@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"
}