• Title of article

    On the role of poetic versus nonpoetic features in “kindred” and diachronic poetry attribution

  • Author/Authors

    Brent D. Fegley1، نويسنده , , 2، نويسنده , , Vetle I. Torvik2، نويسنده ,

  • Issue Information
    ماهنامه با شماره پیاپی سال 2012
  • Pages
    17
  • From page
    2165
  • To page
    2181
  • Abstract
    Author attribution studies have demonstrated remarkable success in applying orthographic and lexicographic features of text in a variety of discrimination problems. What might poetic features, such as syllabic stress and mood, contribute? We address this question in the context of two different attribution problems: (a) kindred: differentiate Langston Hughes’ early poems from those of kindred poets and (b) diachronic: differentiate Hughes’ early from his later poems. Using a diverse set of 535 generic text features, each categorized as poetic or nonpoetic, correlation-based greedy forward search ranked the features and a support vector machine classified the poems. A small subset of features (∼10) achieved cross-validated precision and recall as high as 87%. Poetic features (rhyme patterns particularly) were nearly as effective as nonpoetic in kindred discrimination, but less effective diachronically. In other words, Hughes used both poetic and nonpoetic features in distinctive ways and his use of nonpoetic features evolved systematically while he continued to experiment with poetic features. These findings affirm qualitative studies attesting to structural elements from Black oral tradition and Black folk music (blues) and to the internal consistency of Hughes’ early poetry.
  • Keywords
    Natural language processing , computational linguistics , Machine learning
  • Journal title
    Journal of the American Society for Information Science and Technology
  • Serial Year
    2012
  • Journal title
    Journal of the American Society for Information Science and Technology
  • Record number

    994752