Extracting key terms from noisy and multitheme documents
Proceedings of the 18th international conference on World wide web
Concept labeling: building text classifiers with minimal supervision
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Short-text domain specific key terms/phrases extraction using an n-gram model with wikipedia
Proceedings of the 21st ACM international conference on Information and knowledge management
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We present a new, ontology-based approach to the automatic text categorization. An important and novel aspect of this approach is that our categorization method does not require a training set, which is in contrast to the traditional statistical and probabilistic methods. In the presented method, the ontology, including the domain concepts organized into hierarchies of categories and interconnected by relationships, as well as instances and connections among them, effectively becomes the classifier. Our method focuses on (i) converting a text document into a thematic graph of entities occurring in the document, (ii) ontological classification of the entities in the graph, and (iii) determining the overall categorization of the thematic graph, and as a result, the document itself. In the presented experiments, we used an RDF ontology constructed from the full English version of Wikipedia. Our experiments, conducted on corpora of Reuters news articles, showed that our training-less categorization method achieved a very good overall accuracy.