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Towards Ontology-based Training-less Multi-label Text Classifi cation

Key:ASNC18
Author:Wael Alkhatib, Saba Sabrin, Svenja Neitzel, and Christoph Rensing
Date:April 2018
Kind:In proceedings - use for conference & workshop papers
Publisher:Springer
Book title:The proceeding of the 23rd International Conference on Applications of Natural Language to Information Systems
Pages:389-396
ISBN:978-3-319-91946-1
Keywords:semantics; statistics; feature selection; ontology; text clas- si cation; typed dependencies.
Number of characters:18717
Abstract:In the under-explored research area of multi-label text clas- si cation. Substantial amount of research in adapting and transforming traditional classi ers to directly handle multi-label datasets has taken place. The performance of traditional statistical and probabilistic classi- ers su ers 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 classi cation. We trans- form the classi cation task into a graph matching problem by develop- ing a shallow domain ontology to be used as a training-less classi er. 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.
URL:https://link.springer.com/chapter/10.1007/978-3-319-91947-8_40

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