Paper: Weakly Supervised Approaches For Ontology Population

ACL ID E06-1003
Title Weakly Supervised Approaches For Ontology Population
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006

We present a weakly supervised approach to automatic Ontology Population from text and compare it with other two unsu- pervised approaches. In our experiments we populate a part of our ontology of Named Entities. We considered two high level categories - geographical locations and person names and ten sub-classes for each category. For each sub-class, from a list of training examples and a syntac- tically parsed corpus, we automatically learn a syntactic model - a set of weighted syntactic features, i.e. words which typ- ically co-occur in certain syntactic posi- tions with the members of that class. The modelisthenusedtoclassifytheunknown Named Entities in the test set. The method is weakly supervised, since no annotated corpus is used in the learning process. We achieved promising result...