1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Psychiatric document retrieval using a discourse-aware model Presenter : Wu, Jia-Hao Authors : Liang-Chih Yu, Chung-Hsien Wu, Fong-Lin Jang Artificial Intelligence (2009) 國立雲林科技大學 National Yunlin University of Science and Technology

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Methodology  A Framework of psychiatric document retrieval  Discourse-aware retrieval model Experiments Conclusion Personal Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation Individuals in their daily life may suffer from negative or stressful life events. Some website provide suggestions for individuals.  Browsing and searching all consultation documents to identify the relevant documents is time consuming and tends to become overwhelming. Money Job Argument death

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective The paper proposes the use of high-level discourse-aware model.  The model can extract from queries and documents to improve the precision of retrieval results about the psychiatric document retrieval.  Some Retrieval models, such as vector space model and Okapi model, but there only consider the word-level information in queries and documents. Consultation Documents Query Recommendation

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Events +Symptoms +RelationsDiscourse = Cause-effectTemporal-effect

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Negative life event identification  Find the patterns from the sentences. Pattern induction  Use the seed patterns from psychiatry web corpora using an evolutionary inference algorithm. SVM classification  Use the SVM to train the patterns and transformed into its corresponding feature vector. →,,,

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Symptom Identification  Word segmentation and Part-Of-Speech (POS) tagging  Semantic dependency graph (SDG) construction.  Semantic label inference. The identification of symptoms is sentence-based. t = (modifier, head, rel modifier,head )

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) P((matters, worry about, goal) | ) is much higher than that in all the other label

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Relation Identification  Cause-effect relation  Temporal relation

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Discourse-aware retrieval model Similarity of events and symptoms

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Similarity of relations The relations are represented by symptom chians.  Use the sequence kernel function to calculate the similarity of two symptom chains.

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Sequence kernel function  Symptom 1 ： S 1 S 2 S 3 S 4  Symptom 2 ： S 3 S 2 S 1  Lengths 2 ： {S 1 S 2,S 1 S 3,S 1 S 4,S 2 S 3,S 2 S 4,S 3 S 4 } & {S 3 S 2,S 3 S 1,S 2 S 1 }  Lengths 3 ： {S 1 S 2 S 3,S 1 S 2 S 4,S 1 S 3 S 4,S 2 S 3 S 4 } & {S 3 S 2 S 1 }

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.)

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments A total of 3650 consultation documents.  20 documents were randomly selected as the test query set.  100 documents were randomly selected as the tuning set.  The remaining 3530 documents were the reference set to be retrieved. Use the discounted cumulative gain to evaluate the retrieval models.  Level 0 : No discourse units are matched.  Level 1 : At least one discourse unit is partially matched.  Level 2 : All of the three discourse units are partially matched.  Level 3 : All of the three discourse units are partially matched, and at last one discourse unit is exactly matched.

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion The discourse information can provide more precise information about users’ depressive problems. The psychiatric document retrieval can support psychological treatments, so people can learn self-help skills to alleviate their symptoms. The proposed framework can also be applied to other domains.

17. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments Advantage  The proposed content is easy to know, and the authors use some instances to explain their ideas. Drawback  … Application  Psychological document retrieval.  Information Retrieval.