An Unsupervised Approach to Extract Life-Events from Personal Narratives in the Mental Health Domain

Personal Narratives are an important source of knowledge in the mental health domain. Over an extended period of time, the psychologist learns about the patient’s life-events and participants from the Personal Narratives shared during each therapy session. The acquired knowledge is then used to support the patient to reach a healthier mental state by appropriate targeted feedback during each conversation. In this work, we propose an unsupervised approach to automatically extract personal life-events and participants from the patient’s narratives and represent them as a personal graph. This personal graph is then updated at each interaction with the patient. We have evaluated our proposed approach on a dataset of longitudinal Italian Personal Narratives as well as a dataset of English commonsense stories.