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Báo cáo khoa học: "HAL-based Cascaded Model for Variable-Length Semantic Pattern Induction from Psychiatry Web Resources"

Negative life events play an important role in triggering depressive episodes. Developing psychiatric services that can automatically identify such events is beneficial for mental health care and prevention. Before these services can be provided, some meaningful semantic patterns, such as , have to be extracted. In this work, we present a text mining framework capable of inducing variable-length semantic patterns from unannotated psychiatry web resources. | HAL-based Cascaded Model for Variable-Length Semantic Pattern Induction from Psychiatry Web Resources Liang-Chih Yu and Chung-Hsien Wu Fong-Lin Jang Department of Computer Science and Information Engineering Department of Psychiatry National Cheng Kung University Chi-Mei Medical Center Tainan Taiwan R.O.C. Tainan Taiwan R.O.C. lcyu chwu @csie.ncku.edu.tw jcj0429@seed.net.tw Abstract Negative life events play an important role in triggering depressive episodes. Developing psychiatric services that can automatically identify such events is beneficial for mental health care and prevention. Before these services can be provided some meaningful semantic patterns such as lost parents have to be extracted. In this work we present a text mining framework capable of inducing variable-length semantic patterns from unannotated psychiatry web resources. This framework integrates a cognitive motivated model Hyperspace Analog to Language HAL to represent words as well as combinations of words. Then a cascaded induction process CIP bootstraps with a small set of seed patterns and incorporates relevance feedback to iteratively induce more relevant patterns. The experimental results show that by combining the HAL model and relevance feedback the CIP can induce semantic patterns from the unannotated web corpora so as to reduce the reliance on annotated corpora. 1 Introduction Depressive disorders have become a major threat to mental health. People in their daily life may suffer from some negative or stressful life events such as death of a family member arguments with a spouse loss of a job and so forth. Such life events play an important role in triggering depressive symptoms such as depressed mood suicide attempts and anxiety. Therefore it is desired to develop a system capable of identifying negative life events to provide more effective psychiatric services. For example through the negative life events the health professionals can know the background information about subjects .