Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics

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Traditional unsupervised topic modeling approaches like Latent Dirichlet Allocation (LDA) lack the ability to classify documents into a predefined set of topics. On the other hand, supervised methods require significant amounts of labeled data to perform well on such tasks. We develop a new unsupervised method based on word embeddings to classify documents into predefined topics. We evaluate the predictive performance of this novel approach and compare it to seeded LDA. We use a real-world dataset from online advertising, which is comprised of markedly short documents. Our results indicate the two methods may complement one another well, leading to remarkable sensitivity and precision scores of ensemble learners trained thereupon.
Original languageEnglish
Title of host publicationHuman Practice. Digital Ecologies. Our Future : 14. Internationale Tagung Wirtschaftsinformatik (WI 2019), Tagungsband
EditorsThomas Ludwig, Volkmar Pipek
Number of pages15
Place of PublicationSiegen
PublisherUniversitätsverlag Siegen
Publication date2019
Pages453-467
ISBN (electronic)978-3-96182-063-4
DOIs
Publication statusPublished - 2019
Event14. Internationale Tagung Wirtschaftsinformatik - WI 2019: Human Practice. Digital Ecologies. Our Future. - Universität Siegen, Institut für Wirtschaftsinformatik, Siegen, Germany
Duration: 24.02.201927.02.2019
Conference number: 14
https://wi2019.de/
https://wi2019.de/call-for-papers/
https://wi2019.de/

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