• DocumentCode
    3315469
  • Title

    Neural Networks for Author Attribution

  • Author

    Tsimboukakis, Nikos ; Tambouratzis, George

  • Author_Institution
    Inst. of Language & Speech Process, Athens
  • fYear
    2007
  • fDate
    23-26 July 2007
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The present article investigates the effectiveness of neural network models when applied to the task of categorising texts in the Greek language based on the style of their authors. Multilayer perceptrons (MLP), radial basis function networks (RBF) and self-organizing maps (SOM) are comparatively studied on the task of classifying documents based on a set of countable stylistic features. This task is of particular importance for information retrieval applications that involve very large databases of documents where the manual classification is extremely labour-intensive.
  • Keywords
    database management systems; information retrieval; multilayer perceptrons; natural language processing; neural nets; radial basis function networks; self-organising feature maps; Greek language; author attribution; documents databases; information retrieval applications; multilayer perceptrons; neural networks; radial basis function networks; self-organizing maps; text categorisation; Frequency; Information retrieval; Multilayer perceptrons; Natural languages; Neural networks; Radial basis function networks; Self organizing feature maps; Spatial databases; Speech processing; Statistical analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International
  • Conference_Location
    London
  • ISSN
    1098-7584
  • Print_ISBN
    1-4244-1209-9
  • Electronic_ISBN
    1098-7584
  • Type

    conf

  • DOI
    10.1109/FUZZY.2007.4295356
  • Filename
    4295356