Identification of Causal Dependencies by using Natural Language Processing: A Survey

Erika Nazaruka

Abstract

Identification of cause-effect relations in the domain is crucial for construction of its correct model, and especially for the Topological Functioning Model (TFM). Key elements of the TFM are functional characteristics of the system and cause-effect relations between them. Natural Language Processing (NLP) can help in automatic processing of textual descriptions of functionality of the domain. The current research illustrates results of a survey of research papers on identification and extracting cause-effect relations from text using NLP and other techniques. The survey shows that expression of cause-effect relations in text can be very different. Sometimes the same language constructs indicate both causal and non-causal relations. Hybrid solutions that use machine learning, ontologies, linguistic and syntactic patterns as well as temporal reasoning show better results in extracting and filtering cause-effect pairs. Multi cause and multi effect domains still are not very well studied.

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Paper Citation


in Harvard Style

Nazaruka E. (2019). Identification of Causal Dependencies by using Natural Language Processing: A Survey.In Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: MDI4SE, ISBN 978-989-758-375-9, pages 603-613. DOI: 10.5220/0007842706030613


in Bibtex Style

@conference{mdi4se19,
author={Erika Nazaruka},
title={Identification of Causal Dependencies by using Natural Language Processing: A Survey},
booktitle={Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: MDI4SE,},
year={2019},
pages={603-613},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007842706030613},
isbn={978-989-758-375-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: MDI4SE,
TI - Identification of Causal Dependencies by using Natural Language Processing: A Survey
SN - 978-989-758-375-9
AU - Nazaruka E.
PY - 2019
SP - 603
EP - 613
DO - 10.5220/0007842706030613