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  • × author_ss:"Dumais, S."
  1. Gordon, M.D.; Dumais, S.: Using latent semantic indexing for literature based discovery (1998) 0.02 
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    Abstract
    Latent semantic indexing (LSI) is a statistical technique for improving information retrieval effectiveness. Here, we use LSI to assist in literature-based discoveries. The idea behind literature-based discoveries is that different authors have already published certain underlying scientific ideas that, when taken together, can be connected to hypothesize a new dicovery, and that these connections can be made by exploring the scientific literature. We explore latent semantic indexing's effectiveness on 2 discovery processes: uncovering 'nearby' relationships that are necessary to initiate the literature based discovery process; and discovering more distant relationships that may genuinely generate new discovery hypotheses
    Date
    11. 2.2016 16:22:19
    Type
    a
  2. Teevan, J.; Dumais, S.: Web retrieval, ranking and personalization (2011) 0.00 
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    Type
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  3. Dumais, S.; Chen, H.: Hierarchical classification of Web content (2000) 0.00 
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  4. Deerwester, S.; Dumais, S.; Landauer, T.; Furnass, G.; Beck, L.: Improving information retrieval with latent semantic indexing (1988) 0.00 
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    Abstract
    Describes a latent semantic indexing (LSI) approach for improving information retrieval. Most document retrieval systems depend on matching keywords in queries against those in documents. The LSI approach tries to overcome the incompleteness and imprecision of latent relations among terms and documents. Tested performance of the LSI method ranged from considerably better than to roughly comparable to performance based on weighted keyword matching, apparently depending on the quality of the queries. Best LSI performance was found using a global entropy weighting for terms and about 100 dimensions for representing terms, documents and queries.
    Type
    a

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