Paper: Unsupervised Ontology Induction from Text

ACL ID P10-1031
Title Unsupervised Ontology Induction from Text
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

Extracting knowledge from unstructured text is a long-standing goal of NLP. Al- though learning approaches to many of its subtasks have been developed (e.g., pars- ing, taxonomy induction, information ex- traction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scal- ability. We present OntoUSP, a system that induces and populates a probabilistic on- tology using only dependency-parsed text as input. OntoUSP builds on the USP unsupervised semantic parser by jointly forming ISA and IS-PART hierarchies of lambda-form clusters. The ISA hierar- chy allows more general knowledge to be learned, and the use of smoothing for parameter estimation. We evaluate On- toUSP by using it to extract a knowledge base from biomedical abstracts ...