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.

本文提出了一种新颖的半监督方法，用于从输入词汇表中识别领域分类体系。该方法使用了一种基于概率的框架来提取上位词子序列，通过实验证明在四种语言中，作者的方法优于目前最先进的分类体系识别方法，并且在输入词汇中有噪声的情况下表现出很好的鲁棒性。