UoR-NCL at SemEval-2023 Task 1: Learning Word-Sense and Image Embeddings for Word Sense Disambiguation
Thanet Markchom, Huizhi Liang, Joyce Gitau, Zehao Liu, Varun Ojha, Lee Taylor, Jake Bonnici, Abdullah Alshadadi
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task-1 - visual word sense disambiguation (visual-wsd) Paper
TLDR:
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
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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.