This paper extends previous work on document retrieval and document type classification, addressing the problem of \u2018typed search\u2019. Specifically, given a query and a designated document type, the search system retrieves and ranks documents not only based on the relevance to the query, but also based on the likelihood of being the designated document type. The paper formalizes the problem in a general framework consisting of \u2018relevance model\u2019 and \u2018type model\u2019. The relevance model indicates whether or not a document is relevant to a query. The type model indicates whether or not a document belongs to the designated document type. We consider three methods for combing the models: linear combination of scores, thresholding on the type score, and a hybrid of the previous two methods. We take course page search and instruction document search as examples and have conducted a series of experiments. Experimental results show our proposed approaches can significantly outperform the baseline methods.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper extends previous work on document retrieval and document type classification, addressing the problem of \u2018typed search\u2019. Specifically, given a query and a designated document type, the search system retrieves and ranks documents not only based on the relevance to the query, but also based on the likelihood of being the designated document type. 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