@inproceedings{hossain-etal-2023-garner,
title = "gar{NER} at {S}em{E}val-2023: Simplified Knowledge Augmentation for Multilingual Complex Named Entity Recognition",
author = "Hossain, Md Zobaer and
So, Averie Ho Zoen and
Silwal, Silviya and
Gonzalez Gongora, H. Andres and
Samin, Ahnaf Mozib and
Junaed, Jahedul Alam and
Mazumder, Aritra and
Saha, Sourav and
Tahsin Soha, Sabiha",
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-1.114",
doi = "10.18653/v1/2023.semeval-1.114",
pages = "823--835",
abstract = "This paper presents our solution, garNER, to the SemEval-2023 MultiConer task. We propose a knowledge augmentation approach by directly querying entities from the Wikipedia API and appending the summaries of the entities to the input sentence. These entities are either retrieved from the labeled training set (Gold Entity) or from off-the-shelf entity taggers (Entity Extractor). Ensemble methods are then applied across multiple models to get the final prediction. Our analysis shows that the added contexts are beneficial only when such contexts are relevant to the target-named entities, but detrimental when the contexts are irrelevant.",
}
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%0 Conference Proceedings
%T garNER at SemEval-2023: Simplified Knowledge Augmentation for Multilingual Complex Named Entity Recognition
%A Hossain, Md Zobaer
%A So, Averie Ho Zoen
%A Silwal, Silviya
%A Gonzalez Gongora, H. Andres
%A Samin, Ahnaf Mozib
%A Junaed, Jahedul Alam
%A Mazumder, Aritra
%A Saha, Sourav
%A Tahsin Soha, Sabiha
%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 hossain-etal-2023-garner
%X This paper presents our solution, garNER, to the SemEval-2023 MultiConer task. We propose a knowledge augmentation approach by directly querying entities from the Wikipedia API and appending the summaries of the entities to the input sentence. These entities are either retrieved from the labeled training set (Gold Entity) or from off-the-shelf entity taggers (Entity Extractor). Ensemble methods are then applied across multiple models to get the final prediction. Our analysis shows that the added contexts are beneficial only when such contexts are relevant to the target-named entities, but detrimental when the contexts are irrelevant.
%R 10.18653/v1/2023.semeval-1.114
%U https://aclanthology.org/2023.semeval-1.114
%U https://doi.org/10.18653/v1/2023.semeval-1.114
%P 823-835
Markdown (Informal)
[garNER at SemEval-2023: Simplified Knowledge Augmentation for Multilingual Complex Named Entity Recognition](https://aclanthology.org/2023.semeval-1.114) (Hossain et al., SemEval 2023)
ACL
- Md Zobaer Hossain, Averie Ho Zoen So, Silviya Silwal, H. Andres Gonzalez Gongora, Ahnaf Mozib Samin, Jahedul Alam Junaed, Aritra Mazumder, Sourav Saha, and Sabiha Tahsin Soha. 2023. garNER at SemEval-2023: Simplified Knowledge Augmentation for Multilingual Complex Named Entity Recognition. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 823–835, Toronto, Canada. Association for Computational Linguistics.