Evaluating text coherence based on the graph of the consistency of phrases to identify symptoms of schizophrenia
This work addresses the detection of mental illness symptoms, specifically schizophrenia, through text analysis, but it appears incremental as it builds on existing coherence methods without claiming major breakthroughs.
The paper tackled the problem of detecting schizophrenia symptoms by analyzing text coherence, proposing a method based on a graph of phrase consistency to evaluate semantic coherence and cohesion, along with other linguistic features, and trained a classifier on English-language interviews, achieving results that indicate potential use in mental illness detection tasks.
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.