Psychiatric document retrieval using a discourse-aware model

With the increased incidence of depression-related disorders, many psychiatric websites have been developed to provide huge amounts of educational documents along with rich self-help information. Psychiatric document retrieval aims to assist individuals to locate documents relevant to their depressive problems efficiently and effectively. By referring to relevant documents, individuals can understand how to alleviate their depression-related symptoms according to recommendations from health professionals. This work proposes the use of high-level discourse information extracted from queries and documents to improve the precision of retrieval results. The discourse information adopted herein includes negative life events, depressive symptoms and semantic relations between symptoms, which are beneficial for better understanding of users' queries. Experimental results show that the discourse-aware retrieval model achieves higher precision than the word-based retrieval models, namely the vector space model (VSM) and Okapi model, adopting word-level information alone.