Semi-supervised Learning of Action Ontology from Domain-Specific Corpora

Abstract

The paper presents research results, showing how unsupervised and supervised ontology learning methods can be combined in an action ontology building approach. A framework for action ontology building from domainspecific corpus texts is suggested, using different natural language processing techniques, such as collocation extraction, frequency lists, word space model, etc. The suggested framework employs additional knowledge sources of WordNet and VerbNet with structured linguistic and semantic information. Results from experiments with crawled chemical laboratory corpus texts are given. © Springer-Verlag Berlin Heidelberg 2013.