NON-STATISTICAL METHODS OF AUTOMATIC EXTRACTION OF CAUSAL RELATIONSHIPS FROM THE TEXT
Abstract
Most of the first attempts to extraction of causal relationship were tied with complex and manual
linguistic patterns, syntactic rules and small datasets based on domain. This article examines the paradigm
of a non-statistical approach to the extraction of causal relationships, its basis, language constructs,
patterns, and classification of causal relationships. The aim was to study the methods of this
paradigm, to determine their disadvantages, advantages, and the possibility of their application.The article discusses various approaches given by the authors of well-known and highly cited research
papers and their impact on the success of the extraction of causal relationships. The analysis of these
scientific papers has unequivocally confirmed that the task of extracting CR is an extremely difficult task
of natural language processing. The presence of a variety of linguistic constructions of the language,
ambiguities of various kinds, as well as language features greatly affect the accuracy of CR extraction.
Almost all non-statistical methods have encountered the problem of highly specialized fields of
knowledge, where expert description is almost always required. Also, almost all non-statistical methods
are manual or semi-automatic, because assume the construction of templates for determining the CR in
the text. Even though non-static methods with sufficient accuracy (on average 70-80%) successfully
cope with the task under consideration, there is currently no universal method for extracting CR.
The proposed method should be universal with respect to languages, universal with respect to subject
areas and with the possibility of defining implicit CR.
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