Türker, RimaZhang, LeiKoutraki, MariaSack, HaraldDavid, KlausGeihs, KurtLange, MartinStumme, Gerd2019-08-272019-08-272019978-3-88579-688-6https://dl.gi.de/handle/20.500.12116/24994Short text categorization is an important task due to the rapid growth of online available short texts in various domains such as web search snippets, news feeds, etc. Most of the traditional methods suffer from sparsity and shortness of the text. Moreover, supervised learning methods require a significant amount of training data and manually labeling such data can be very time-consuming and costly. In this study, we propose a novel probabilistic model for Knowledge-Based Short Text Categorization (KBSTC), which does not require any labeled training data to categorize a short text [Tü].enShort Text CategorizationDataless Text ClassificationNetwork EmbeddingsText/Conference Paper10.18420/inf2019_451617-5468