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PsychoNet: a psycholinguistc commonsense ontology

By Haytham Mohtasseb and Amr Ahmed

Abstract

Ontologies have been widely accepted as the most advanced knowledge representation model. This paper introduces PsychoNet, a new knowledgebase that forms the link between psycholinguistic taxonomy, existing in LIWC, and its semantic textual representation in the form of commonsense semantic ontology, represented by ConceptNet. The integration of LIWC and ConceptNet and the added functionalities facilitate employing ConceptNet in psycholinguistic studies. Furthermore, it simplifies utilization of the huge network of ConceptNet for a specific multimedia application based on key category(ies) from LIWC, such as visual or biological applications. PsychoNet adds a new layer of complementary psycholinguistic functions to the original semantic network. Moreover, learning, either clustering or classification, is more applicable in the developed ontology. The paper shows a sample application of text classification for mood prediction task. The result confirms the validity of the proposed network as PsychoNet outperforms LIWC in mood prediction

Topics: G700 Artificial Intelligence, G760 Machine Learning, G400 Computer Science, G720 Knowledge Representation, Q150 Psycholinguistics
Year: 2010
OAI identifier: oai:eprints.lincoln.ac.uk:3589

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