TY - JOUR
T1 - Psychiatric document retrieval using a discourse-aware model
AU - Yu, Liang Chih
AU - Wu, Chung Hsien
AU - Jang, Fong Lin
N1 - Funding Information:
This work was supported by the National Science Council, Taiwan, ROC, under Grant No. NSC 97-2218-E-155-011. The authors would like to thank the anonymous reviewers and the associate editor for their constructive comments.
PY - 2009/5
Y1 - 2009/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=62549112558&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62549112558&partnerID=8YFLogxK
U2 - 10.1016/j.artint.2008.12.004
DO - 10.1016/j.artint.2008.12.004
M3 - Article
AN - SCOPUS:62549112558
SN - 0004-3702
VL - 173
SP - 817
EP - 829
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 7-8
ER -