Training-less Multi-label Text Classification Using Knowledge Bases and Word Embeddings
Key: ASC19-1
Author: Wael Alkhatib, Steffen Schnitzer, and Christoph Rensing
Date: May 2019
Kind: In proceedings
Publisher: Springer
Book title: The 12th International Conference on Knowledge Science, Engineering and Management (KSEM 2019)
Keywords: semantics, knowledge base, ontology, text classification
Abstract: Traditional multi-label text classifiers suffer from the high dimensionality of feature space, label imbalance, and training overhead. In this work, we depart from traditional approaches with intensive feature engineering and linguistic analysis by introducing a novel ontology-based training-less multi-label text classifier. We transform the classification task into a graph matching problem to have a training-less classifier. The experiment results, using the EUR-Lex dataset, proved that our method offers competitive performance with respect to the above-mentioned approaches in terms of F1-macro giving fair performance over the different labels despite of the training-less configurations.
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