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Multilabel text classification of research methods using Deep Transfer Learning for Natural Language Processing

  • This thesis examines the effectiveness of the latest Transfer Learning techniques for Natural Language Processing applied to the classification of research methods used in scientific journals in the domain of Information Systems. The task of automated knowledge extraction from academic articles has seen ongoing progress in recent years. However, the combination of transfer and Deep Learning in order to assign research methods to scientific papers has not been addressed in the literature yet. The main contribution of this thesis is, therefore, an artifact that applies cutting-edge Transfer Learning techniques to a Deep Learning model by conducting several experiments and comparing their effectiveness. The prototype considers various ways of fine-tuning that are crucial to retain the knowledge transferred from pretrained models and avoid catastrophic forgetting. Additionally, this work discusses the literature with regard to the task-specific Theory Ontology Learning and the method-specific state of the art in Transfer Learning for Natural Language Processing. As a result, the artifact surpassed the performance of previously developed models for research method extraction, presented in the literature, without applying any custom feature engineering and only using around a thousand of labeled observations.

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Metadaten
Author:Anna Anisienia
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:2019/01/30
Release Date:2019/08/13
Tag:BERT; CNN; Conv1D; Deep Learning; Deep Transfer Learning; ELMo; GloVe; LSTM; Research method extraction; Theory Ontology Learning; Transfer Learning; ULMFiT; multilabel classification
Page Number:109
Institutes:FB I - Wirtschaftswissenschaften / Business Intelligence and Process Management M.Sc.
Licence (German):License LogoUrheberrechtsschutz