Modern approaches to the mechanisms of causal relationships extraction from unstructured natural language texts
Abstract
The article is devoted to the study of methods of extracting causal relationships from unstructured text in a natural language. Automatic extraction of causal relationships from natural language texts is a complex open problem in artificial intelligence. The ways of expressing explicit causal relationships used in information extraction technologies are described. The article provides a brief overview of modern data mining support tools that allow classifying data, using statistical analysis methods, clustering and segmentation tools, using visualization tools, as well as text analysis and information retrieval packages.The authors of the article present the results of developing a method for extracting cause-and-effect relationships based on the existing IBM Watson and StanfordCoreNLP cognitive services using the Natural Language Understanding, StanfordParse, Natural Language Classifier and Stanford Classifier services. The article describes a software package designed to investigate various methods for detecting causal connections in natural language texts. The software toolkit included in this complex is implemented in IBM Bluemix cloud infrastructure and provides a set of services that allow you to extract and classify relationships in unstructured text, test the analyzed methods, use administrative tools for working with services and data.
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