Published: 2018 Dezember
Buchtitel: Proc. 21st International Conference on Knowledge Engineering and Knowledge Management 2018 (EKAW 2018)
Verlag: CEUR Workshop Proceedings
Text classification is an important and challenging task due news filtering. Several supervised learning approaches have been proposed for text classification. However, most of them require a significant amount of training data. Manually labeling such data can be very time-consuming and costly. To overcome the problem of labeled data, we demonstrate TECNE, a knowledge-based text classification method using network embeddings. The proposed system does not require any labeled training data to classify an arbitrary text. Instead, it relies on a set of predefined categories to determine a category which the given document belongs to.
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