Abstract
Taxonomies are an essential knowledge representation, yet most studies on automatic taxonomy construction (ATC) resort to manual evaluation to score proposed algorithms. We argue that automatic taxonomy evaluation (ATE) is just as important as taxonomy construction. We propose RaTE, an automatic label-free taxonomy scoring procedure, which relies on a large pre-trained language model. We apply our evaluation procedure to three state-of-the-art ATC algorithms with which we built seven taxonomies from the Yelp domain, and show that 1) RaTE correlates well with human judgments and 2) artificially degrading a taxonomy leads to decreasing RaTE score.- Anthology ID:
- 2023.iwcs-1.18
- Volume:
- Proceedings of the 15th International Conference on Computational Semantics
- Month:
- June
- Year:
- 2023
- Address:
- Nancy, France
- Editors:
- Maxime Amblard, Ellen Breitholtz
- Venue:
- IWCS
- SIG:
- SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 173–182
- Language:
- URL:
- https://aclanthology.org/2023.iwcs-1.18
- DOI:
- Cite (ACL):
- Phillippe Langlais and Tianjian Lucas Gao. 2023. RaTE: a Reproducible automatic Taxonomy Evaluation by Filling the Gap. In Proceedings of the 15th International Conference on Computational Semantics, pages 173–182, Nancy, France. Association for Computational Linguistics.
- Cite (Informal):
- RaTE: a Reproducible automatic Taxonomy Evaluation by Filling the Gap (Langlais & Gao, IWCS 2023)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2023.iwcs-1.18.pdf