Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings
This work addresses taxonomy refinement for natural language processing applications, but it is incremental as it builds on existing approaches with a specific embedding method.
The paper tackled the problem of domain-specific taxonomy induction from text by using Poincaré embeddings to refine taxonomies, resulting in substantial improvements over previous state-of-the-art results on the SemEval-2016 Task 13 benchmark.
We introduce the use of Poincaré embeddings to improve existing state-of-the-art approaches to domain-specific taxonomy induction from text as a signal for both relocating wrong hyponym terms within a (pre-induced) taxonomy as well as for attaching disconnected terms in a taxonomy. This method substantially improves previous state-of-the-art results on the SemEval-2016 Task 13 on taxonomy extraction. We demonstrate the superiority of Poincaré embeddings over distributional semantic representations, supporting the hypothesis that they can better capture hierarchical lexical-semantic relationships than embeddings in the Euclidean space.