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2020 | OriginalPaper | Buchkapitel

An Overview of Ways of Discovering Cause-Effect Relations in Text by Using Natural Language Processing

verfasst von : Erika Nazaruka

Erschienen in: Evaluation of Novel Approaches to Software Engineering

Verlag: Springer International Publishing

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Abstract

Understanding of cause-effect relations is vital for constructing a valid model of a system under development. Discovering cause-effect relations in text is one of the difficult tasks in Natural Language Processing (NLP). This paper presents a survey on trends in this field related to understanding how linguistically causal dependencies can be expressed in the text, what patterns and models exist, which of them are more and less successful and why. The results show that causal dependencies in text can be described using plenty lexical expressions as well as linguistic and syntactic patterns. Moreover, the same constructs can be used for non-causal dependencies. Solutions that combine the patterns, ontologies, temporal models and a use of machine learning demonstrate more accurate results in extracting and selecting cause-effect pairs. However, not all lexical expressions are well studied. There are few researches on multi-cause and multi-effect domains. The results of the survey are to be used for construction of a Topological Functioning Model (TFM) of a system, where cause-effect relations are one of key elements. However, they can be used also for construction of other behavioral models.

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Titel: An Overview of Ways of Discovering Cause-Effect Relations in Text by Using Natural Language Processing
verfasst von: Erika Nazaruka
Verlag: Springer International Publishing
Buch: Evaluation of Novel Approaches to Software Engineering
Print ISBN: 978-3-030-40222-8

Electronic ISBN: 978-3-030-40223-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-40223-5_2

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