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

ACL ID D13-1018
Title Growing Multi-Domain Glossaries from a Few Seeds using Probabilistic Topic Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

In this paper we present a minimally- supervised approach to the multi-domain ac- quisition of wide-coverage glossaries. We start from a small number of hypernymy rela- tion seeds and bootstrap glossaries from the Web for dozens of domains using Probabilis- tic Topic Models. Our experiments show that we are able to extract high-precision glos- saries comprising thousands of terms and def- initions.