Abstract
This article presents our solution for SemEval-2023 Task 1: Visual Word Sense Disambiguation. The aim of the task was to select the most suitable from a list of ten images for a given word, extended by a small textual context. Our solution comprises two parts. The first focuses on an attempt to further extend the textual context, based on word definitions contained in WordNet and in Open English WordNet. The second focuses on selecting the most suitable image using the CLIP model with previously developed word context and additional information obtained from the BEiT image classification model. Our solution allowed us to achieve a result of 70.84% on the official test dataset for the English language.- Anthology ID:
- 2023.semeval-1.67
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 482–487
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.67
- DOI:
- 10.18653/v1/2023.semeval-1.67
- Cite (ACL):
- Małgorzata Grębowiec. 2023. OPI PIB at SemEval-2023 Task 1: A CLIP-based Solution Paired with an Additional Word Context Extension. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 482–487, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- OPI PIB at SemEval-2023 Task 1: A CLIP-based Solution Paired with an Additional Word Context Extension (Grębowiec, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.semeval-1.67.pdf