DeepCause: Hypothesis Extraction from Information Systems Papers with Deep Learning for Theory Ontology Learning

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dc.contributor.authorMueller, Roland
dc.contributor.authorAbdullaev, Sardor
dc.date.accessioned2019-01-03T00:47:20Z
dc.date.available2019-01-03T00:47:20Z
dc.date.issued2019-01-08
dc.description.abstractThis paper applies different deep learning architectures for sequence labelling to extract causes, effects, moderators, and mediators from hypotheses of information systems papers for theory ontology learning. We compared a variety of recurrent neural networks (RNN) architectures, like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), simple RNNs, and gated recurrent units (GRU). We analyzed GloVe word embedding, character level vector representation of words, and part-of-speech (POS) tags. Furthermore, we evaluated various hyperparameters and architectures to achieve the highest performance scores. The prototype was evaluated on hypotheses from the AIS basket of eight. The F1 result for the sequence labelling task of causal variables on a chunk level was 80%, with a precision of 80% and a recall of 80%.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.752
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/60059
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectKnowing What We Know: Theory, Meta-analysis, and Review
dc.subjectOrganizational Systems and Technology
dc.subjectCausal Relation Extraction, Deep Learning, Natural Language Processing, Sequence Labelling, Theory Ontology Learning
dc.titleDeepCause: Hypothesis Extraction from Information Systems Papers with Deep Learning for Theory Ontology Learning
dc.typeConference Paper
dc.type.dcmiText

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