The paper considers the use of elements of Formal Concept Analysis – multidimensional or polyadic formal contexts – to extract information from natural language texts. We propose the method for constructing polyadic formal contexts by means of Semantic Role Labeling and Abstract Meaning Representation (AMR) of texts. Using semantic role labeling, a conceptual graph is created for each sentence of the text, and a specific scheme of abstract meaning representation of the sentence is developed based on its elements. The polyadic formal context is a multidimensional tensor, whose points are elements of an AMR scheme. To extract information from a polyadic formal context, data associations as sub-contexts of the original context are built. Each such subcontext is associated with a specific element of the AMR scheme. Queries to associations return responses that preserve the meaning of the phrases according to the AMR scheme. The method was tested in the task of finding dependencies between texts on the corpus of abstracts of scientific articles on biomedical subjects of the PubMed system.