@inproceedings{markchom-etal-2023-uor,
title = "{U}o{R}-{NCL} at {S}em{E}val-2023 Task 1: Learning Word-Sense and Image Embeddings for Word Sense Disambiguation",
author = "Markchom, Thanet and
Liang, Huizhi and
Gitau, Joyce and
Liu, Zehao and
Ojha, Varun and
Taylor, Lee and
Bonnici, Jake and
Alshadadi, Abdullah",
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-1.3",
doi = "10.18653/v1/2023.semeval-1.3",
pages = "16--22",
abstract = "In SemEval-2023 Task 1, a task of applying Word Sense Disambiguation in an image retrieval system was introduced. To resolve this task, this work proposes three approaches: (1) an unsupervised approach considering similarities between word senses and image captions, (2) a supervised approach using a Siamese neural network, and (3) a self-supervised approach using a Bayesian personalized ranking framework. According to the results, both supervised and self-supervised approaches outperformed the unsupervised approach. They can effectively identify correct images of ambiguous words in the dataset provided in this task.",
}
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<abstract>In SemEval-2023 Task 1, a task of applying Word Sense Disambiguation in an image retrieval system was introduced. To resolve this task, this work proposes three approaches: (1) an unsupervised approach considering similarities between word senses and image captions, (2) a supervised approach using a Siamese neural network, and (3) a self-supervised approach using a Bayesian personalized ranking framework. According to the results, both supervised and self-supervised approaches outperformed the unsupervised approach. They can effectively identify correct images of ambiguous words in the dataset provided in this task.</abstract>
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%0 Conference Proceedings
%T UoR-NCL at SemEval-2023 Task 1: Learning Word-Sense and Image Embeddings for Word Sense Disambiguation
%A Markchom, Thanet
%A Liang, Huizhi
%A Gitau, Joyce
%A Liu, Zehao
%A Ojha, Varun
%A Taylor, Lee
%A Bonnici, Jake
%A Alshadadi, Abdullah
%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 markchom-etal-2023-uor
%X In SemEval-2023 Task 1, a task of applying Word Sense Disambiguation in an image retrieval system was introduced. To resolve this task, this work proposes three approaches: (1) an unsupervised approach considering similarities between word senses and image captions, (2) a supervised approach using a Siamese neural network, and (3) a self-supervised approach using a Bayesian personalized ranking framework. According to the results, both supervised and self-supervised approaches outperformed the unsupervised approach. They can effectively identify correct images of ambiguous words in the dataset provided in this task.
%R 10.18653/v1/2023.semeval-1.3
%U https://aclanthology.org/2023.semeval-1.3
%U https://doi.org/10.18653/v1/2023.semeval-1.3
%P 16-22
Markdown (Informal)
[UoR-NCL at SemEval-2023 Task 1: Learning Word-Sense and Image Embeddings for Word Sense Disambiguation](https://aclanthology.org/2023.semeval-1.3) (Markchom et al., SemEval 2023)
ACL
- Thanet Markchom, Huizhi Liang, Joyce Gitau, Zehao Liu, Varun Ojha, Lee Taylor, Jake Bonnici, and Abdullah Alshadadi. 2023. UoR-NCL at SemEval-2023 Task 1: Learning Word-Sense and Image Embeddings for Word Sense Disambiguation. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 16–22, Toronto, Canada. Association for Computational Linguistics.