Taxonomy Induction using Hypernym Subsequences
This work addresses the challenge of building taxonomies from noisy data for applications in natural language processing and knowledge organization, offering a novel and robust solution.
The paper tackles the problem of domain taxonomy induction from seed terms by proposing a semi-supervised approach that extracts hypernym subsequences and formulates induction as a minimum-cost flow problem, outperforming state-of-the-art methods across four languages and demonstrating robustness to noise in the input vocabulary.
We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary.