@inproceedings{misra-boye-2024-nested-noun,
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="misra-boye-2024-nested-noun">
<titleInfo>
<title>Nested Noun Phrase Identification Using BERT</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shweta</namePart>
<namePart type="family">Misra</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Johan</namePart>
<namePart type="family">Boye</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicoletta</namePart>
<namePart type="family">Calzolari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Yen</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakriani</namePart>
<namePart type="family">Sakti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nianwen</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">misra-boye-2024-nested-noun</identifier>
<location>
<url>https://aclanthology.org/2024.lrec-main.1062</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>12138</start>
<end>12143</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Nested Noun Phrase Identification Using BERT
%A Misra, Shweta
%A Boye, Johan
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F misra-boye-2024-nested-noun
%X 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.
%U https://aclanthology.org/2024.lrec-main.1062
%P 12138-12143
Markdown (Informal)
[Nested Noun Phrase Identification Using BERT](https://aclanthology.org/2024.lrec-main.1062) (Misra & Boye, LREC-COLING 2024)
ACL
- Shweta Misra and Johan Boye. 2024. Nested Noun Phrase Identification Using BERT. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12138–12143, Torino, Italia. ELRA and ICCL.