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Review Authorship Attribution in a Similarity Space

Abstract

Authorship attribution, also known as authorship classification, is the problem of identifying the authors (reviewers) of a set of documents (reviews). The common approach is to build a classifier using supervised learning. This approach has several issues which hurts its applicability. First, supervised learning needs a large set of documents from each author to serve as the training data. This can be difficult in practice. For example, in the online review domain, most reviewers (authors) only write a few reviews, which are not enough to serve as the training data. Second, the learned classifier cannot be applied to authors whose documents have not been used in training. In this article, we propose a novel solution to deal with the two problems. The core idea is that instead of learning in the original document space, we transform it to a similarity space. In the similarity space, the learning is able to naturally tackle the issues. Our experiment results based on online reviews and reviewers show that the proposed method outperforms the state-of-the-art supervised and unsupervised baseline methods significantly.

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References

  1. [1]

    Grieve J. Quantitative authorship attribution: An evaluation of techniques. Literary and Linguistic Computing, 2007, 22(3): 251-270.

  2. [2]

    Baayen H, van Halteren H, Tweedie F. Outside the cave of shadows: Using syntactic annotation to enhance authorship attribution. Literary and Linguistic Computing, 1996, 11(3): 121-132.

  3. [3]

    Argamon S, Whitelaw C, Chase P, Hota S R, Garg N, Levitan S. Stylistic text classification using functional lexical features: Research articles. Journal of the Association for Information Science and Technology, 2007, 58(6): 802-822.

  4. [4]

    Hedegaard S, Simonsen J G. Lost in translation: Authorship attribution using frame semantics. In Proc. the 49th ACL, June 2011, pp. 65-70.

  5. [5]

    Hirst G, Feiguina O. Bigrams of syntactic labels for authorship discrimination of short texts. Literary and Linguistic Computing, 2007, 22(4): 405-417.

  6. [6]

    Holmes D I, Forsyth R S. The federalist revisited: New directions in authorship attribution. Literary and Linguistic Computing, 1995, 10(2): 111-127.

  7. [7]

    Koppel M, Schler J. Authorship verification as a one-class classification problem. In Proc. the 21st ICML, July 2004.

  8. [8]

    Diederich J, Kindermann J, Leopold E, Paass G. Authorship attribution with support vector machines. Applied Intelligence, 2000, 19(1/2): 109-123.

  9. [9]

    Escalante H J, Solorio T, Montes-y-Gómez M. Local histograms of character n-grams for authorship attribution. In Proc. the 49th ACL, June 2011, pp. 288-298.

  10. [10]

    Li J, Zheng R, Chen H. From fingerprint to writeprint. Communications of the ACM, 2006, 49(4): 76-82.

  11. [11]

    Stamatatos E, Fakotakis N, Kokkinakis G. Automatic text categorization in terms of genre and author. Computational Linguistics, 2000, 26(3): 471-495.

  12. [12]

    Graham N, Hirst G, Marthi B. Segmenting documents by stylistic character. Natural Language Engineering, 2005, 11(4): 397-415.

  13. [13]

    Seroussi Y, Bohnert F, Zukerman I. Authorship attribution with author-aware topic models. In Proc. the 50th ACL, July 2012, pp. 264-269.

  14. [14]

    de Vel O, Anderson A, Corney M, Mohay G. Mining e-mail content for author identification forensics. ACM SIGMOD Record, 2001, 30(4): 55-64.

  15. [15]

    Koppel M, Schler J, Argamon S. Authorship attribution in the wild. Language Resources and Evaluation, 2011, 45(1): 83-94.

  16. [16]

    Solorio T, Pillay S, Raghavan S, y Gómez M M. Modality specific meta features for authorship attribution in Web forum posts. In Proc. the 5th IJCNLP, Nov. 2011, pp. 156-164.

  17. [17]

    Kim S, Kim H, Weninger T, Han J, Kim H D. Authorship classification: A discriminative syntactic tree mining approach. In Proc. the 34th SIGIR, July 2011, pp. 455-464.

  18. [18]

    Jindal N, Liu B. Opinion spam and analysis. In Proc. WSDM, Feb. 2008, pp. 219-230.

  19. [19]

    Rudin C. The p-norm push: A simple convex ranking algorithm that concentrates at the top of the list. The Journal of Machine Learning Research, 2009, 10: 2233-2271.

  20. [20]

    Yih W, Meek C. Improving similarity measures for short segments of text. In Proc. AAAI, Nov. 2007, pp. 1489-1494.

  21. [21]

    Agichtein E, Brill E, Dumais S T, Ragno R. Learning user interaction models for predicting web search result preferences. In Proc. the 29th SIGIR, Aug. 2006, pp. 3-10.

  22. [22]

    Mosteller F, Wallace D L. Inference and Disputed Authorship: The Federalist. Addison-Wesley, 1964.

  23. [23]

    Argamon S, Levitan S. Measuring the usefulness of function words for authorship attribution. In Proc. the 2005 ACH/ALLC Conference, June 2005.

  24. [24]

    Gamon M. Linguistic correlates of style: Authorship classification with deep linguistic analysis features. In Proc. the 20th COLING, Aug. 2004, Article No. 611.

  25. [25]

    Peng F, Schuurmans D, Wang S, Keselj V. Language independent authorship attribution using character level language models. In Proc. EACL, April 2003, pp. 267-274.

  26. [26]

    Burrows J F. Not unless you ask nicely: The interpretative nexus between analysis and information. Literary and Linguistic Computing, 1992, 7(2): 91-109.

  27. [27]

    Sanderson C, Guenter S. Short text authorship attribution via sequence kernels, Markov chains and author unmasking: An investigation. In Proc. EMNLP, July 2006, pp. 482-491.

  28. [28]

    Madigan D, Genkin A, Lewis D, Argamon S, Fradkin D, Ye L. Author identification on the large scale. In Proc. CSNA, June 2005.

  29. [29]

    Cao Y, Xu J, Liu T, Li H, Huang Y, Hon H. Adapting ranking SVM to document retrieval. In Proc. the 29th SIGIR, Oct. 2006, pp. 186-193.

  30. [30]

    Stamatatos E. A survey of modern authorship attribution methods. Journal of the Association for Information Science and Technology, Aug. 2009, 60(3): 538-556.

  31. [31]

    Hoover D L. Statistical stylistics and authorship attribution: An empirical investigation. Literary and Linguistic Computing, 2001, 16(4): 421-444.

  32. [32]

    Zheng R, Li J, Chen H, Huang Z. A framework for authorship identification of online messages: Writing style features and classification techniques. Journal of the Association for Information Science and Technology, 2006, 57(3): 378-393.

  33. [33]

    Uzuner Ö, Katz B. A comparative study of language models for book and author recognition. In Proc. the 2nd IJCNLP, Oct. 2005, pp. 969-980.

  34. [34]

    Zhao Y, Zobel J. Effective and scalable authorship attribution using function words. In Proc. the 2nd Asia Information Retrieval Symposium, Oct. 2005, pp. 174-189.

  35. [35]

    Luyckx K, Daelemans W. Authorship attribution and verification with many authors and limited data. In Proc. the 22nd COLING, Aug. 2008, pp. 513-520.

  36. [36]

    Vapnik V N. Statistical Learning Theory. Wiley-Interscience, 1998.

  37. [37]

    Graepely T, Herbrichz R, Bollmann-Sdorraz P, Obermayery K. Classification on pairwise proximity data. In Proc. NIPS, Jan. 1999, pp. 438-444.

  38. [38]

    Chen Y, Garcia E K, Gupta M R, Rahimi A, Cazzanti L. Similarity-based classification: Concepts and algorithms. The Journal of Machine Learning Research, 2009, 10: 747-776.

  39. [39]

    Pezkalska E, Duin R P W. Dissimilarity representations allow for building good classifiers. Pattern Recognition Letters, 2002, 23(8): 943-956.

  40. [40]

    Liao L, Noble W S. Combining pairwise sequence similarity and support vector machines for remote protein homology detection. In Proc. the 6th RECOMB, April 2002, pp. 225-232.

  41. [41]

    Wang L, Yang C, Feng J. On learning with dissimilarity functions. In Proc. the 24th ICML, June 2007, pp. 991-998.

  42. [42]

    Balcan M F, Blum A, Srebro N. A theory of learning with similarity functions. Machine Learning, 2008, 72(1/2): 89-112.

  43. [43]

    Kar P, Jain P. Similarity-based learning via data driven embeddings. In Proc. the 25th NIPS, Dec. 2011.

  44. [44]

    Yule G U. The Statistical Study of Literary Vocabulary. Cambridge University Press, 1944.

  45. [45]

    Metzler D, Bernstein Y, Croft W B, Moffat A, Zobel J. Similarity measures for tracking information flow. In Proc. the 14th CIKM, Oct. 2005, pp. 517-524.

  46. [46]

    Joachims T. Training linear SVMs in linear time. In Proc. the 12th KDD, Aug. 2006, pp. 217-226.

  47. [47]

    Klein D, Manning C D. Accurate unlexicalized parsing. In Proc. the 41st ACL, July 2003, pp. 423-430.

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Author information

Correspondence to Qing Li.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61272275, 61232002, 61272110, 61202036, 61379004, 61472337, and 61028003, and the 111 Project of China under Grant No. B07037.

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Cite this article

Qian, T., Liu, B., Li, Q. et al. Review Authorship Attribution in a Similarity Space. J. Comput. Sci. Technol. 30, 200–213 (2015). https://doi.org/10.1007/s11390-015-1513-6

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Keywords

  • authorship attribution
  • supervised learning
  • similarity space