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Short Text Classification Using Universal Language Model Fine-Tuning

  • Deep Learning, a topic of broad and current interest, has undergone rapid development in the last decade. The performance of the algorithms is already above human level. However, the area of Natural Language Processing is still a great challenge for the researchers. This study aims at exploring the novel ULMFiT method for text classification by applying it to a dataset of scientific articles with advanced rhetorical categories. This classification task is challenging even for humans and it requires a substantial analysis of the texts when conducted by machine learning algorithms. The objective of this attempt is to achieve text classification with minimal preparation. The ULMFiT method is the first successful effort to apply transfer learning to NLP tasks. Its performance will be evaluated on a task that requires a level of understanding beyond the semantic meaning of the text.

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Metadaten
Author:Stella Valcheva
Referee:Roland Müller
Advisor:Markus Löcher
Document Type:Master's Thesis
Language:English
Date of first Publication:2019/08/13
Publishing Institution:Hochschulbibliothek HWR Berlin
Granting Institution:Hochschule für Wirtschaft und Recht Berlin
Date of final exam:2019/01/23
Release Date:2019/08/13
Tag:Argumentative Zoning; Deep Learning; LSTM; Language Model; NLP; Text Classification; Transfer Learning
Page Number:65
Institutes:FB I - Wirtschaftswissenschaften / Business Intelligence and Process Management M.Sc.
Licence (German):License LogoUrheberrechtsschutz