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Hypothesis Extraction from Academic Papers Using Neural Networks for Ontology Theory Learning

  • In this study, we investigated a new use case of deep learning. We applied deep learning to extract causes and effects from the hypotheses of the scientific papers. The research presents a variety of RNN models, including RNN models with CRF layer for labelling the sequences. We used such models as Bi-LSTM, LSTM, SimpleRNN and GRU. The experiments were conducted with GloVe vector representation and character level vector representation of words. Moreover, along with RNN models, we evaluated various hyperparameters and model setups to achieve the highest performance scores. In the end, we obtained promising results and shared our thoughts on the future prospects of the following studies.

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Metadaten
Author:Sardor Abdullaev
Referee:Roland Müller
Advisor:Markus Löcher
Document Type:Master's Thesis
Language:English
Date of first Publication:2019/09/03
Publishing Institution:Hochschulbibliothek HWR Berlin
Granting Institution:Hochschule für Wirtschaft und Recht Berlin
Date of final exam:2018/01/24
Release Date:2019/09/03
Tag:Causal Relation Extraction; Deep Learning; GRU; GloVe; LSTM; RNN; Sequence labelling
Page Number:107
Institutes:FB I - Wirtschaftswissenschaften / Business Intelligence and Process Management M.Sc.
Licence (German):License LogoUrheberrechtsschutz