Towards Ontology-Based Training-Less Multi-label Text Classification
In the under-explored research area of multi-label text classification. Substantial amount of research in adapting and transforming traditional classifiers to directly handle multi-label datasets has taken place. The performance of traditional statistical and probabilistic classifiers suffers from the high dimensionality of feature space, training overhead and label imbalance. In this work, we propose a novel ontology-based approach for training-less multi-label text classification. We transform the classification task into a graph matching problem by developing a shallow domain ontology to be used as a training-less classifier. 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 performance to the state-of-the-art techniques in terms of Macro \(F_1\)-Score.
KeywordsSemantics Statistics Feature selection Ontology Text classification Typed dependencies
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