  Different state-of-the-art methods of the detection of schizophrenia symptoms
based on the estimation of text coherence have been analyzed. The analysis of a
text at the level of phrases has been suggested. The method based on the graph
of the consistency of phrases has been proposed to evaluate the semantic
coherence and the cohesion of a text. The semantic coherence, cohesion, and
other linguistic features (lexical diversity, lexical density) have been taken
into account to form feature vectors for the training of a model-classifier.
The training of the classifier has been performed on the set of
English-language interviews. According to the retrieved results, the impact of
each feature on the output of the model has been analyzed. The results obtained
can indicate that the proposed method based on the graph of the consistency of
phrases may be used in the different tasks of the detection of mental illness.
