@inproceedings{elkaref-etal-2023-nlpeople,
title = "{NLP}eople at {S}em{E}val-2023 Task 2: A Staged Approach for Multilingual Named Entity Recognition",
author = "Elkaref, Mohab and
Herr, Nathan and
Tanaka, Shinnosuke and
De Mel, Geeth",
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-1.159/",
doi = "10.18653/v1/2023.semeval-1.159",
pages = "1148--1153",
abstract = "The MultiCoNER II shared task aims at detecting complex, ambiguous named entities with fine-grained types in a low context setting. Previous winning systems incorporated external knowledge bases to retrieve helpful contexts. In our submission we additionally propose splitting the NER task into two stages, a Span Extraction Step, and an Entity Classification step. Our results show that the former does not suffer from the low context setting comparably, and in so leading to a higher overall performance for an external KB-assisted system. We achieve 3rd place on the multilingual track and an average of 6th place overall."
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<abstract>The MultiCoNER II shared task aims at detecting complex, ambiguous named entities with fine-grained types in a low context setting. Previous winning systems incorporated external knowledge bases to retrieve helpful contexts. In our submission we additionally propose splitting the NER task into two stages, a Span Extraction Step, and an Entity Classification step. Our results show that the former does not suffer from the low context setting comparably, and in so leading to a higher overall performance for an external KB-assisted system. We achieve 3rd place on the multilingual track and an average of 6th place overall.</abstract>
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%0 Conference Proceedings
%T NLPeople at SemEval-2023 Task 2: A Staged Approach for Multilingual Named Entity Recognition
%A Elkaref, Mohab
%A Herr, Nathan
%A Tanaka, Shinnosuke
%A De Mel, Geeth
%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 elkaref-etal-2023-nlpeople
%X The MultiCoNER II shared task aims at detecting complex, ambiguous named entities with fine-grained types in a low context setting. Previous winning systems incorporated external knowledge bases to retrieve helpful contexts. In our submission we additionally propose splitting the NER task into two stages, a Span Extraction Step, and an Entity Classification step. Our results show that the former does not suffer from the low context setting comparably, and in so leading to a higher overall performance for an external KB-assisted system. We achieve 3rd place on the multilingual track and an average of 6th place overall.
%R 10.18653/v1/2023.semeval-1.159
%U https://aclanthology.org/2023.semeval-1.159/
%U https://doi.org/10.18653/v1/2023.semeval-1.159
%P 1148-1153
Markdown (Informal)
[NLPeople at SemEval-2023 Task 2: A Staged Approach for Multilingual Named Entity Recognition](https://aclanthology.org/2023.semeval-1.159/) (Elkaref et al., SemEval 2023)
ACL