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|Author:||Wael Alkhatib, Saba Sabrin, Svenja Neitzel, and Christoph Rensing|
|Kind:||In proceedings - use for conference & workshop papers|
|Book title:||The proceeding of the 23rd International Conference on Applications of Natural Language to Information Systems|
|Keywords:||semantics; statistics; feature selection; ontology; text clas- sication; typed dependencies.|
|Number of characters:||18717|
|Abstract:||In the under-explored research area of multi-label text clas- sication. Substantial amount of research in adapting and transforming traditional classiers to directly handle multi-label datasets has taken place. The performance of traditional statistical and probabilistic classi- ers suers from the high dimensionality of feature space, training over- head and label imbalance. In this work, we propose a novel ontology- based approach for training-less multi-label text classication. We trans- form the classication task into a graph matching problem by develop- ing a shallow domain ontology to be used as a training-less classier. Thereby, we overcome the challenges of feature engineering and label imbalance of traditional methods. Our intensive experiments, using the EUR-Lex dataset, prove that our method provides a comparable perfor- mance to the state-of-the-art techniques in terms of Macro F1-Score.|
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