In this paper we present a Multilingual Ontology-Driven framework for Text Classification (MOoD-TC). This framework is highly modular and can be customized to create applications based on Multilingual Natural Language Processing for classifying domain-dependent contents. In order to show the potential of MOoD-TC, we present a case study in the e-Health domain.
Identification of disease symptoms in multilingual sentences: An ontology-driven approach / Ferrando, Angelo; Beux, Silvio; Mascardi, Viviana; Rosso, Paolo. - 1589:(2016), pp. 6-15. (Intervento presentato al convegno 1st Workshop on Modeling, Learning and Mining for Cross/Multilinguality, MultiLingMine 2016 tenutosi a Padova nel 20 marzo 2016).
Identification of disease symptoms in multilingual sentences: An ontology-driven approach
FERRANDO, ANGELO;
2016
Abstract
In this paper we present a Multilingual Ontology-Driven framework for Text Classification (MOoD-TC). This framework is highly modular and can be customized to create applications based on Multilingual Natural Language Processing for classifying domain-dependent contents. In order to show the potential of MOoD-TC, we present a case study in the e-Health domain.
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