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Mapping Knowledge Domains (2004) / Chapter Skim
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Mapping subsets of scholarly information
Pages 54-58

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
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From page 54...
... Together with the SLAC SPIRES-HEP database, it provides a public resource of full-text articles and associated citation trees of many millions of links, with a focused disciplinary coverage and rich usage data. tThe Stanford Linear Accelerator Center SPIRES-HEP database has comprehensively catalogued the High Energy Particle Physics (HEP)
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From page 55...
... The optimal value of this parameter depends on the particular classification task and must be chosen by means of cross-validation or by some other model selection strategy. For text classification, however, the default value of C = 1/maxi~i~2 = 1 has proven to be effective across a large range of tasks (11~.
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From page 56...
... We also used a document frequency threshold to exclude rare words from the lexicon but found little difference in accuracy between using a document occurrence threshold of two and five. (Words that appeared in fewer than two documents constituted ~50% of the lexicon, and those that appeared in Ginsparg eta/.
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From page 57...
... . epidemic biology disease cell neural brain ecosystem tissue sequence genetic bacterial blood genome peptide infection +8.57 +7.08 +5.06 + 5.05 +3.30 +3.21 +3.1 5 +3.1 1 +3.05 +3.02 +2.93 +2.90 +2.89 +2.83 +2.56 +2.52 _ +2.51 d saakian -0.00 +2.51 conformity -0.00 +2.48 aware -0.00 +2.43 even though -0.00 +2.37 practitioner -0.00 +2.37 permittivity -0.00 +2.34 forward +0.00 minimalist +0.00 region +0.00 confinement +0.00 implies +0.00 96 +0.00 y togashi +0.00 n wingreen +0.00 mean free +0.00 narrower +0.00 shot -0.00 repton -0.00 kyoto -0.00 regular -0.00 ceneralization-O.OO point equation boundary social n relaxation fluid Indian spin spin glass traffic system polymer class emerge gradient quantum surface synchronizatio market particle polyelectrolyte world —1.04 —1.05 —1.06 —1.06 —1.09 —1.1 4 —1.15 —1.15 —1.17 —1.17 —1.18 —1.30 —1.33 —1.35 —1.36 —1.39 —1.43 —1.43 - 1 .45 —1.47 —1.52 —1.53 —1.57 PNAS 1 April 6, 2004 1 vol 101 1 suppl.
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From page 58...
... , and use it to stake intellectual priority claims in advance of journal publication. We see further that machine learning tools can characterize a subdomain and thereby help accelerate its growth by the interaction of an information resource with the social system of its practitioners.
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Key Terms

  • machine learning
  • support vector
  • text classification
  • weight vector
  • training set

  • document frequency
  • training examples
  • statistical learning
  • beckman center
  • national acaclemies

  • training error
  • frequency threshold
  • academic literature
  • training example
  • vector machine

  • classifying weight
  • learning approach
  • idf weighting
  • training errors
  • learning theory

  • tfx idf
  • component values
  • knowledge discovery
  • classification task
  • learning methods

  • vector machines
  • online corpus
  • dot product
  • mabel beckman
  • text classifier


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