Intelligent Database Systems Lab N.Y.U.S.T. I. M. Psychiatric document retrieval using a...
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![Page 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.](https://reader035.fdocuments.us/reader035/viewer/2022062409/5697bfd21a28abf838cab69e/html5/thumbnails/1.jpg)
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
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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
![Page 3: 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.](https://reader035.fdocuments.us/reader035/viewer/2022062409/5697bfd21a28abf838cab69e/html5/thumbnails/3.jpg)
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.
MoneyJob
Argume
nt
death
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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.
ConsultationDocuments
Query Recommendation
![Page 5: 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.](https://reader035.fdocuments.us/reader035/viewer/2022062409/5697bfd21a28abf838cab69e/html5/thumbnails/5.jpg)
Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
Methodology
Events + Symptoms + RelationsDiscourse =Cause-effectTemporal-effect
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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.
<Husband, argue> →<Husband, fight> ,<husband, yell>,<wife, argue> , <husband, fight, money>
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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 , relmodifier,head)
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
Methodology (Cont.)
P((matters, worry about , goal) | <Anxiety>) is much higher than that in all the other label
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
Methodology (Cont.)
Relation Identification Cause-effect relation
Temporal relation
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
Methodology (Cont.)
Discourse-aware retrieval model
Similarity of events and symptoms
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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.
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
Methodology (Cont.)
Sequence kernel function Symptom 1 : S1S2S3S4
Symptom 2 : S3S2S1
Lengths 2 : {S1S2,S1S3,S1S4,S2S3,S2S4,S3S4} & {S3S2,S3S1,S2S1}
Lengths 3 : {S1S2S3,S1S2S4,S1S3S4,S2S3S4} & {S3S2S1}
1)()( 321321 3221 ssssss ssss
1321 )(
31 sssss
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
Methodology (Cont.)
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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.
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Intelligent Database Systems Lab
N.Y.U.S.T.I. M.
Experiments
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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.
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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.