Abstract
This paper extends previous work on document retrieval and document type classification, addressing the problem of ’typed search’. 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 ’relevance model’ and ’type model’. 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.
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Xu, J., Cao, Y., Li, H., Craswell, N., Huang, Y. (2007). Searching Documents Based on Relevance and Type. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_60
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DOI: https://doi.org/10.1007/978-3-540-71496-5_60
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