Summary of the paper

Title Using the Complexity of the Distribution of Lexical Elements as a Feature in Authorship Attribution
Authors Leanne Spracklin, Diana Inkpen and Amiya Nayak
Abstract Traditional Authorship Attribution models extract normalized counts of lexical elements such as nouns, common words and punctuation and use these normalized counts or ratios as features for author fingerprinting. The text is viewed as a “bag-of-words” and the order of words and their position relative to other words is largely ignored. We propose a new method of feature extraction which quantifies the distribution of lexical elements within the text using Kolmogorov complexity estimates. Testing carried out on blog corpora indicates that such measures outperform ratios when used as features in an SVM authorship attribution model. Moreover, by adding complexity estimates to a model using ratios, we were able to increase the F-measure by 5.2-11.8%
Language Single language
Topics Text mining, Document Classification, Text categorisation, Information Extraction, Information Retrieval
Full paper Using the Complexity of the Distribution of Lexical Elements as a Feature in Authorship Attribution
Slides Using the Complexity of the Distribution of Lexical Elements as a Feature in Authorship Attribution
Bibtex @InProceedings{SPRACKLIN08.892,
  author = {Leanne Spracklin, Diana Inkpen and Amiya Nayak},
  title = {Using the Complexity of the Distribution of Lexical Elements as a Feature in Authorship Attribution},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  month = {may},
  date = {28-30},
  address = {Marrakech, Morocco},
  editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-4-0},
  note = {http://www.lrec-conf.org/proceedings/lrec2008/},
  language = {english}
  }

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