@inproceedings{mehta-varma-2023-llm,
title = "{LLM}-{RM} at {S}em{E}val-2023 Task 2: Multilingual Complex {NER} Using {XLM}-{R}o{BERT}a",
author = "Mehta, Rahul and
Varma, Vasudeva",
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-1.62",
doi = "10.18653/v1/2023.semeval-1.62",
pages = "453--456",
abstract = "Named Entity Recognition(NER) is a task ofrecognizing entities at a token level in a sen-tence. This paper focuses on solving NER tasksin a multilingual setting for complex named en-tities. Our team, LLM-RM participated in therecently organized SemEval 2023 task, Task 2:MultiCoNER II,Multilingual Complex NamedEntity Recognition. We approach the problemby leveraging cross-lingual representation pro-vided by fine-tuning XLM-Roberta base modelon datasets of all of the 12 languages provided - Bangla, Chinese, English, Farsi, French,German, Hindi, Italian, Portuguese, Spanish,Swedish and Ukrainian.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mehta-varma-2023-llm">
<titleInfo>
<title>LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rahul</namePart>
<namePart type="family">Mehta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vasudeva</namePart>
<namePart type="family">Varma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Atul</namePart>
<namePart type="given">Kr.</namePart>
<namePart type="family">Ojha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">A</namePart>
<namePart type="given">Seza</namePart>
<namePart type="family">Doğruöz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giovanni</namePart>
<namePart type="family">Da San Martino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Harish</namePart>
<namePart type="family">Tayyar Madabushi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ritesh</namePart>
<namePart type="family">Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elisa</namePart>
<namePart type="family">Sartori</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Named Entity Recognition(NER) is a task ofrecognizing entities at a token level in a sen-tence. This paper focuses on solving NER tasksin a multilingual setting for complex named en-tities. Our team, LLM-RM participated in therecently organized SemEval 2023 task, Task 2:MultiCoNER II,Multilingual Complex NamedEntity Recognition. We approach the problemby leveraging cross-lingual representation pro-vided by fine-tuning XLM-Roberta base modelon datasets of all of the 12 languages provided - Bangla, Chinese, English, Farsi, French,German, Hindi, Italian, Portuguese, Spanish,Swedish and Ukrainian.</abstract>
<identifier type="citekey">mehta-varma-2023-llm</identifier>
<identifier type="doi">10.18653/v1/2023.semeval-1.62</identifier>
<location>
<url>https://aclanthology.org/2023.semeval-1.62</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>453</start>
<end>456</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa
%A Mehta, Rahul
%A Varma, Vasudeva
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F mehta-varma-2023-llm
%X Named Entity Recognition(NER) is a task ofrecognizing entities at a token level in a sen-tence. This paper focuses on solving NER tasksin a multilingual setting for complex named en-tities. Our team, LLM-RM participated in therecently organized SemEval 2023 task, Task 2:MultiCoNER II,Multilingual Complex NamedEntity Recognition. We approach the problemby leveraging cross-lingual representation pro-vided by fine-tuning XLM-Roberta base modelon datasets of all of the 12 languages provided - Bangla, Chinese, English, Farsi, French,German, Hindi, Italian, Portuguese, Spanish,Swedish and Ukrainian.
%R 10.18653/v1/2023.semeval-1.62
%U https://aclanthology.org/2023.semeval-1.62
%U https://doi.org/10.18653/v1/2023.semeval-1.62
%P 453-456
Markdown (Informal)
[LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa](https://aclanthology.org/2023.semeval-1.62) (Mehta & Varma, SemEval 2023)
ACL