Growing Multi-Domain Glossaries from a Few Seeds using Probabilistic Topic Models

In this paper we present a minimallysupervised approach to the multi-domain acquisition of wide-coverage glossaries. We start from a small number of hypernymy relation seeds and bootstrap glossaries from the Web for dozens of domains using Probabilistic Topic Models. Our experiments show that we are able to extract high-precision glossaries comprising thousands of terms and definitions.