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.