A comparative study on authorship attribution classification tasks using both neural network and statistical methods

Neural Computing and Applications

Volumn 19, Issue 4, 2010, Pages 573-582

  Tsimboukakis, Nikos ^a   Tambouratzis, George ^a  

^a INSTITUTE FOR LANGUAGE AND SPEECH PROCESSING   (Greece)

Author keywords

Document classification; Feature selection; Neural networks

Indexed keywords

EID: 77952877494     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-009-0314-7     Document Type: Article

References (22)

1. Gurney PJ, Gurney LW (1998) Subsets and homogeneity: authorship attribution in the Scriptores Historiae Augustae. Lit Linguist Comput 13(3): 133-140.
2. Holmes DI (1994) Authorship attribution. Comput Humanit 28: 86-106.
3. Mosteller F, Wallace DL (1984) Applied Bayesian and classical inference: the case of the Federalist papers, 2nd edn. Springer, New York.
4. Holmes DI, Singh S, Tweedie FJ (1996) Neural network applications in stylometry: the Federalist papers. Comput Humanit 30: 1-10.
5. Lowe D, Matthews R (1995) Shakespeare vs. Fletcher: a stylometric analysis by radial basis functions. Comput Humanit 29: 449-461.
6. Tambouratzis G, Hairetakis N, Markantonatou S, Carayannis G (2003) Applying the SOM model to text classification according to register and stylistic content. Int J Neural Syst 13(1): 1-11.
7. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood Cliffs.
8. Somers H, Tweedie F (2003) Authorship attribution and practice. Comput Humanit 37: 407-429.
9. Tambouratzis G, Markantonatou S, Hairetakis N, Vassiliou M, Tambouratzis D, Carayannis G (2004) Discriminating the registers and styles in the Modern Greek language-part 2: extending the feature vector to optimise author discrimination. Lit Linguist Comput 19(2): 221-242.
10. Kolman E, Margaliot M (2005) Are artificial neural networks white boxes? IEEE Trans Neural Netw 16(4): 844-852.
11. Mackay D (1992) A practical Bayesian framework for backpropagation networks. Neural Comput 4(3): 448-472.
12. Nguyen D, Widrow B (1990) Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. Proc Int Jt Conf Neural Netw 3: 21-26.
13. Papageorgiou H, Prokopidis P, Giouli V, Piperidis S (2000) A unified PoS tagging architecture and its application to Greek, vol 3. Second international conference on language resources and evaluation proceedings. Athens, pp 1455-1462.
14. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York.
15. Fahlman S, Lebiere C (1990) The cascade-correlation learning architecture. Adv Neural Inform Process Syst 2: 524-532 Morgan Kaufmann.
16. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inform Process Manag 24(5): 513-523.
17. Vapnik V (1998) Statistical learning theory. Wiley Interscience, New York.
18. Diederich J, Kinderman J, Leopold E, Paass G (2003) Authorship attribution with support vector machines. Appl Intell 19: 109-123.
19. Bhamidipati NL, Pal SK (2007) Stemming via distribution-based word segregation for classification and retrieval. IEEE Trans Syst Man Cybern B Cybern 37(2): 350-360.
20. Drucker H, Wu D, Vapnik V (1999) Support vector machines for spam categorization. IEEE Trans Neural Netw 10(5): 1048-1054.
21. Koprinska I, Poon J, Clark J, Chan J (2007) Learning to classify e-mail. Inform Sci 177(10): 2167-2187.
22. Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. Software available at http://www. csie. ntu. edu. tw/~cjlin/libsvm.