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  • × author_ss:"Gordon, M.D."
  1. Gordon, M.D.; Dumais, S.: Using latent semantic indexing for literature based discovery (1998) 0.13 
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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
  2. Gordon, M.D.; Lindsay, R.K.: Toward discovery support systems : a replication, re-examination, and extension of Swanson's work on literature-based discovery of a connection between Raynaud's and fish oil (1996) 0.07 
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    Abstract
    Replicates Swanson's discovery of a connection between Raynaud's disease and dietary fish oil. Develops computer based searching methods that could usefully support literature based doscoveries
  3. Fan, W.; Gordon, M.D.; Pathak, P.: ¬A generic ranking function discovery framework by genetic programming for information retrieval (2004) 0.05 
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    Abstract
    Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.
  4. Lindsay, R.K.; Gordon, M.D.: Literature-based discovery by lexical statistics (1999) 0.03 
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