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.
|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 17 → 2006 7月 18
|Conference||21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006|
|期間||06-07-17 → 06-07-18|
All Science Journal Classification (ASJC) codes