Topic Analysis for Psychiatric Document Retrieval

Psychiatric document retrieval attempts to help people to efficiently and effectively locate the consultation documents relevant to their depressive problems. Individuals can understand how to alleviate their symptoms according to recommendations in the relevant documents. This work proposes the use of high-level topic information extracted from consultation documents to improve the precision of retrieval results. The topic 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 proposed approach achieves higher precision than the word-based retrieval models, namely the vector space model (VSM) and Okapi model, adopting word-level information alone.