Topic Analysis for Psychiatric Document Retrieval

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 1024–1031, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Topic Analysis for Psychiatric Document Retrieval Liang-Chih Yu*‡, Chung-Hsien Wu*, Chin-Yew Lin†, Eduard Hovy‡ and Chia-Ling Lin* *Department of CSIE, National Cheng Kung University, Tainan, Taiwan, R.O.C. †Microsoft Research Asia, Beijing, China ‡Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA liangchi@isi.edu,{chwu,totalwhite}@csie.ncku.edu.tw,cyl@microsoft.com,hovy@isi.edu Abstract 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 symp- toms according to recommendations in the relevant documents. This work proposes the use of high-level topic information ex- tracted from consultation documents to im- prove 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 understand- ing of users' queries. Experimental results show that the proposed approach achieves higher precision than the word-based re- trieval models, namely the vector space model (VSM) and Okapi model, adopting word-level information alone. 1 Introduction Individuals may suffer from negative or stressful life events, such as death of a family member, ar- gument with a spouse and loss of a job. Such events play an important role in triggering depres- sive symptoms, such as depressed moods, suicide attempts and anxiety. Individuals under these cir- cumstances can consult health professionals using message boards and other services. Health profes- sionals respond with suggestions as soon as possi- ble. However, the response time is generally sev- eral days, depending on both the processing time required by health professionals and the number of problems to be processed. Such a long response time is unacceptable, especially for patients suffer- ing from psychiatric emergencies such as suicide attempts. A potential solution considers the prob- lems that have been processed and the correspond- ing suggestions, called consultation documents, as the psychiatry web resources. These resources gen- erally contain thousands of consultation documents (problem-response pairs), making them a useful information resource for mental health care and prevention. By referring to the relevant documents, individuals can become aware that they are not alone because many people have suffered from the same or similar problems. Additionally, they can understand how to alleviate their symptoms ac- cording to recommendations. However, browsing and searching all consultation documents to iden- tify the relevant documents is time consuming and tends to become overwhelming. Individuals need to be able to retrieve the relevant consultation documents efficiently and effectively. Therefore, this work presents a novel mechanism to automati- cally retrieve the relevant consultation documents with respect to users' problems. Traditional information retrieval systems repre- sent queries and documents using a bag-of-words approach. Retrieval models, such as the vector space model (VSM) (Baeza-Yates and Ribeiro- Neto, 1999) and Okapi model (Robertson et al., 1995; Robertson et al., 1996; Okabe et al., 2005), are then adopted to estimate the relevance between queries and documents. The VSM represents each …

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