Thomas Vestskov Terney

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This paper describes experiments in using machine learning for relation disambiguation. There have been succesfuld experiments in combining machine learning and ontologies, or light-weight ontologies such as WordNet, for word sense disambiguation. However, what we are trying to do, is to disambiguate complex concepts consisting of two simpler concepts and(More)
In order to facilitate content based information retrieval we need methods for analyzing the semantic structures in the text. In order to facilitate this, we propose a straightforward method for identifying semantic relations between two concepts in an ontology based on supervised machine learning. More specifically we have achieved good results in trying(More)
Recent literature on text-tagging reported successful results by applying Maximum Entropy (ME) models. In general, ME taggers rely on carefully selected binary features, which try to capture discriminant information from the training data. This paper introduces a standard setting of binary features, inspired by the literature on named-entity recognition and(More)
In our preliminary studies we are trying to investigate the possibilities of using a combination of machine learning, ontologyes and an annotated corpus in order to identify semantic relation between noun phrases, e.g. to able to identify that in “vitamin K deficiency causes increased tendency to bleed”, there is a causes relation between the lack of K(More)
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