Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity

In this study, we sought to utilize the embeddings of first-person pronouns obtained from contextualized language representation models to capture ways these pronouns are used, to analyze mental status. De-identified text messages sent during online psychotherapy with weekly assessment of depression severity were used for evaluation. Results indicate the advantage of contextualized first-person pronoun embeddings over standard classification token embeddings and frequency-based pronoun analysis results in predicting depression symptom severity. This suggests contextual representations of first-person pronouns can enhance the predictive utility of language used by people with depression symptoms.PMID:38222407 | PMC:PMC10785936
Source: AMIA Annual Symposium Proceedings - Category: Bioinformatics Authors: Source Type: research