An unsupervised technical difficulty ranking model based on conceptual terrain in the latent space.
JCDL(2012)
摘要
ABSTRACTSearch results of the existing general-purpose search engines usually do not satisfy domain-specific information retrieval tasks as there is a mis-match between the technical expertise of a user and the results returned by the search engine. In this paper, we investigate the problem of ranking domain-specific documents based on the technical difficulty. We propose an unsupervised conceptual terrain model using Latent Semantic Indexing (LSI) for re-ranking search results obtained from a similarity based search system. We connect the sequences of terms under the latent space by the semantic distance between the terms and compute the traversal cost for a document indicating the technical difficulty. Our experiments on a domain-specific corpus demonstrate the efficacy of our method.
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