@inproceedings{carik-etal-2022-su,
title = "{SU}-{NLP} at {S}em{E}val-2022 Task 11: Complex Named Entity Recognition with Entity Linking",
author = "{\c{C}}ar{\i}k, Buse and
Beyhan, Fatih and
Yeniterzi, Reyyan",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.227",
doi = "10.18653/v1/2022.semeval-1.227",
pages = "1648--1653",
abstract = "This paper describes the system proposed by Sabanc{\i} University Natural Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an unsupervised entity linking pipeline that detects potential entity mentions with the help of Wikipedia and also uses the corresponding Wikipedia context to help the classifier in finding the named entity type of that mention. The proposed pipeline significantly improved the performance, especially for complex entities in low-context settings.",
}
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<abstract>This paper describes the system proposed by Sabancı University Natural Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an unsupervised entity linking pipeline that detects potential entity mentions with the help of Wikipedia and also uses the corresponding Wikipedia context to help the classifier in finding the named entity type of that mention. The proposed pipeline significantly improved the performance, especially for complex entities in low-context settings.</abstract>
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%0 Conference Proceedings
%T SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking
%A Çarık, Buse
%A Beyhan, Fatih
%A Yeniterzi, Reyyan
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F carik-etal-2022-su
%X This paper describes the system proposed by Sabancı University Natural Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an unsupervised entity linking pipeline that detects potential entity mentions with the help of Wikipedia and also uses the corresponding Wikipedia context to help the classifier in finding the named entity type of that mention. The proposed pipeline significantly improved the performance, especially for complex entities in low-context settings.
%R 10.18653/v1/2022.semeval-1.227
%U https://aclanthology.org/2022.semeval-1.227
%U https://doi.org/10.18653/v1/2022.semeval-1.227
%P 1648-1653
Markdown (Informal)
[SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking](https://aclanthology.org/2022.semeval-1.227) (Çarık et al., SemEval 2022)
ACL