Conceptual Similarity for Subjective Tags

Yacine Gaci, Boualem Benatallah, Fabio Casati, Khalid Benabdeslem


Abstract
Tagging in the context of online resources is a fundamental addition to search systems. Tags assist with the indexing, management, and retrieval of online products and services to answer complex user queries. Traditional methods of matching user queries with tags either rely on cosine similarity, or employ semantic similarity models that fail to recognize conceptual connections between tags, e.g. ambiance and music. In this work, we focus on subjective tags which characterize subjective aspects of a product or service. We propose conceptual similarity to leverage conceptual awareness when assessing similarity between tags. We also provide a simple cost-effective pipeline to automatically generate data in order to train the conceptual similarity model. We show that our pipeline generates high-quality datasets, and evaluate the similarity model both systematically and on a downstream application. Experiments show that conceptual similarity outperforms existing work when using subjective tags.
Anthology ID:
2022.findings-aacl.5
Volume:
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
54–66
Language:
URL:
https://aclanthology.org/2022.findings-aacl.5
DOI:
10.18653/v1/2022.findings-aacl.5
Bibkey:
Cite (ACL):
Yacine Gaci, Boualem Benatallah, Fabio Casati, and Khalid Benabdeslem. 2022. Conceptual Similarity for Subjective Tags. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 54–66, Online only. Association for Computational Linguistics.
Cite (Informal):
Conceptual Similarity for Subjective Tags (Gaci et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-aacl.5.pdf
Software:
 2022.findings-aacl.5.Software.zip