Semantic domains and supersense tagging for domain-specific ontology learning

Authors:
Davide Picca;Alfio Massimiliano Gliozzo;Massimiliano Ciaramita
Affiliations:
University of Lausanne, Lausanne - Switzerland;Fondazione Bruno Kessler, (TN) Italy;Yahoo! Research Barcelona Ocata Barcelona - Spain
Venue:
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Year:
2007

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Abstract

In this paper we propose a novel unsupervised approach to learning domain-specific ontologies from large open-domain text collections. The method is based on the joint exploitation of Semantic Domains and Super Sense Tagging for Information Retrieval tasks. Our approach is able to retrieve domain specific terms and concepts while associating them with a set of high level ontological types, named supersenses, providing flat ontologies characterized by very high accuracy and pertinence to the domain.