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.
@article{arxiv.2307.09706,
title = {RaTE: a Reproducible automatic Taxonomy Evaluation by Filling the Gap},
author = {Tianjian Gao and Phillipe Langlais},
journal= {arXiv preprint arXiv:2307.09706},
year = {2023}
}
Comments
15th International Conference on Computational Semantics (IWCS), Association for Computational Linguistics (ACL)