Abstract
Psychiatric query document retrieval can assist individuals to locate query documents relevant to their depression-related problems efficiently and effectively. By referring to relevant documents, individuals can understand how to alleviate their depression-related symptoms according to recommendations from health professionals. This work presents an extended probabilistic Hyperspace Analog to Language ( epHAL ) model to achieve this aim. The epHAL incorporates the close temporal associations between words in query documents to represent word cooccurrence relationships in a high-dimensional context space. The information flow mechanism further combines the query words in the epHAL space to infer related words for effective information retrieval. The language model perplexity is considered as the criterion for model optimization. Finally, the epHAL is adopted for psychiatric query document retrieval, and indicates its superiority in information retrieval over traditional approaches.
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