# Taxonomy Induction using Hypernym Subsequences

## TL;DR

This paper introduces a semi-supervised method for domain taxonomy induction that leverages hypernym subsequences within a probabilistic framework, outperforming existing methods and demonstrating robustness to noisy input vocabularies across multiple languages.

## Contribution

It presents a novel hypernym subsequence extraction approach and formulates taxonomy induction as a minimum-cost flow problem, improving robustness and accuracy.

## Key findings

- Outperforms state-of-the-art methods across four languages.
- Demonstrates robustness to noisy input vocabularies.
- Effective in extracting hypernym subsequences for taxonomy induction.

## Abstract

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.