Transfer-based taxonomy induction over concept labels

Given a domain-specific set of concepts, taxonomy induction is the problem of inducing a taxonomy from the set of concepts. The problem, despite having practical importance, has not received as much research attention, in contrast with related problems such as link prediction, due to its difficulty and lack of domain-specific benchmarks. In this paper, we present a principled approach for taxonomy induction in the e-commerce domain over a set of concept-labels, given background resources such as a pre-trained language representation learning model and examples of other taxonomies, induced over other concept-sets, but no example links for the target concept-set. Our approach, developed as an academic-industrial collaboration, is significantly more competitive than seven different baselines, including the transformer-based RoBERTa model, on three real-world and widely used e-commerce concept-sets.

Automated summary

This paper presents a principled approach for taxonomy induction in the e-commerce domain by utilizing a pre-trained language representation learning model and examples of other taxonomies. The proposed method outperforms seven different baselines, including the transformer-based RoBERTa model, on three widely used e-commerce concept-sets.