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Contribution Details
| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Topic Extraction and Visualisation of Digitalisation Related Research from ZORA |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2018 |
| Abstract Text | Due to the fast increasement of available documents in the Internet, methods are needed which are able to present the content of the data, without the need to read them. This methods already exists, called topic models, but tend to work only for large documents. This work analyses current state-of-the-art topic models as well as presenting some own, context-sensitive approaches on a restricted data set built from abstracts. Then, the best results are visualised to improve the interpretability of the data. |
| Zusammenfassung | Durch die stetig wachsende Zunahme an verfugbaren Dokumenten im Internet werden Methoden benotigt, die helfen den Kontext der Daten zu ermitteln, ohne alles zu lesen. Solche Methoden, Topic Models, existieren bereits, funktionieren aber meistens nur fur grosse Dokumente. Diese Arbeit analysiert die momentan benutzten Topic Models, prasentiert aber auch zwei neue, kontextsensitive Moglichkeiten anhand eines Datensatzes der auf Zusammenfassungen basiert. Anschliessend werden die besten Resultate visuell aufbereitet um die Interpretierbarkeit zu verbessern. |
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