HAL-based cascaded model for variable-length semantic pattern induction from psychiatry web resources

Liang Chih Yu, Chung Hsien Wu, Fong Lin Jang

研究成果: Paper同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁面945-952
頁數8
出版狀態Published - 2006
事件21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, Australia
持續時間: 2006 7月 172006 7月 18

Conference

Conference21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006
國家/地區Australia
城市Sydney
期間06-07-1706-07-18

All Science Journal Classification (ASJC) codes

  • 電腦繪圖與電腦輔助設計
  • 電腦視覺和模式識別
  • 建模與模擬
  • 人機介面

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