Stylistic text classification using functional lexical features

Journal of the American Society for Information Science and Technology

Volumn 58, Issue 6, 2007, Pages 802-822

  Argamon, Shlomo ^a   Whitelaw, Casey ^b   Chase, Paul ^a   Hota, Sobhan Raj ^a   Garg, Navendu ^a   Shlomo Levitan, L ^a  

^a Illinois Institute of Technology   (United States)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

LEXICAL FEATURES; SEMANTIC FUNCTIONS; TEXT CLASSIFICATION;

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; FUNCTION EVALUATION; SEMANTICS;

TEXT PROCESSING;

EID: 34247168561     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.20553     Document Type: Article

References (77)

1. Androutsopoulos, I., Koutsias, J., Chandrinos, K., Paliouras, G., & Spyropoulos, C. (2000). An evaluation of naive bayesian anti-spam filtering. In Proceedings of the Workshop on Machine Learning in the New Information Age (pp. 9-17). New York: ACM Press.
2. Appelt, D., Hobbs, J., Bear, J., Israel, D., & Tyson, M. (1993). FASTUS: A finite-state processor for information extraction from real-world text. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1172-1178).
3. Argamon, S., Dodick, J., & Chase, P. (2005). The languages of science: A corpus-based study of experimental and historical science articles. In Proceedings of the 26th Annual Meeting of the Cognitive Science Society (pp. 157-162).
4. Argamon, S., Koppel, M., Fine, J., & Shimony, A.R. (2003). Gender, genre, and writing style in formal written texts. Text, 23(3), 321-346.
5. Argamon, S., & Levitan, S. (2005). Measuring the usefulness of function words for authorship attribution. In Proceedings of the 2005 ACH/ALLC Conference. Retrieved from http://web.uvic.ca/hrd/achallc2005/abstracts.htm
6. Argamon, S., & Olsen, M. (2006, April). Toward meaningful computing. Communications of the ACM, 49(4), 33-35.
7. Baayen, R.H., Halteren, H. van, & Tweedie, F. (1996). Outside the cave of shadows: Using syntactic annotation to enhance authorship attribution. Literary and Linguistic Computing, 7, 91-109.
8. Belkin, N.J. (1993). Interaction with texts: Information retrieval as information-seeking behavior. Information Retrieval, 93, 55-66.
9. Berry, M.J., & Linoff, G. (1997). Data mining techniques: For marketing, sales, and customer support. New York: Wiley.
10. Burrows, J.F. (1987). Computation into criticism: A study of Jane Austen's novels and an experiment in method. Oxford, England: Clarendon Press.
11. Butler, C.S. (2003). Structure and function - A guide to three major structural-functional theories (No. 63-64). Amsterdam: John Benjamins.
12. Chaski, C.E. (1999). Linguistic authentication and reliability. In Proceedings of the National Conference on Science and the Law (pp. 97-148). San Diego, CA: National Institute of Justice.
13. Chen, S.Y., Magoulas, G.D., & Dimakopoulos, D. (2005). A flexible interface design for web directories to accommodate different cognitive styles. Journal of the American Society for Information Science and Technology, 56(1), 70-83.
14. Cowie, J., & Lehnert, W. (1996). Information extraction. Communications of the ACM, 39(1), 80-91.
15. Cristea, D., Marcu, D., Ide, N., & Tablan, V. (1999). Discourse structure and co-reference: An empirical study. In D. Cristea, N. Ide, & D. Marcu (Eds.), The relation of discourse/dialogue structure and reference (pp. 46-53). New Brunswick, NJ: Association for Computational Linguistics.
16. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge: Cambridge University Press.
17. de Vel, O. (2000). Mining e-mail authorship. In ACM International Conference on Knowledge Discovery and Data Mining Workshop on Text Mining. Boston. Retrieved from http://www.cs.cmu.edu/~dunja/WshKDD2000.html
18. de Vel, O., Corney, M., Anderson, A., & Mohay, G. (2002). Language and gender author cohort analysis of e-mail for computer forensics. In Proceedings of the Digital Forensic Research Workshop. Syracuse, NY. (pp. 7-9).
19. Fawcett, R.P., & Tucker, G.H. (1990). Demonstration of GENESYS: A very large, semantically based systemic functional grammar. In Proceedings of the 13th International Conference on Computational Linguistics (COLING-90) (pp. 47-49). Helsinki, Finland.
20. Finn, A., Kushmerick, N., & Smyth, B. (2002). Genre classification and domain transfer for information filtering. In F. Crestani, M. Girolami, & C.J. van Rijsbergen (Eds.), Proceedings of the 24th European Colloquium on Information Retrieval Research. Glasgow, United Kingdom: Springer Verlag, Heidelberg, DE.
21. Firth, J. (1968). A synopsis of linguistic theory 1930-1955. In F. Palmer (Ed.), Selected papers of J.R. Firth 1952-1959. London: Longman.
22. Fritch, J.W., & Cromwell, R.L. (2001). Evaluating internet resources: Identity, affiliation, and cognitive authority in a networked world. Journal of the American Society for Information Science and Technology, 52(6), 498-507.
23. Grossman, D., & Frieder, O. (1998). Information retrieval: Algorithms and heuristics. Dordrecht, The Netherlands: Kluwer.
24. Halliday, M.A.K. (1994). Introduction to functional grammar (2nd ed.). London: Arnold.
25. Halliday, M.A.K., & Hasan, R. (1976). Cohesion in English. London: Longman.
26. Holmes, D.I. (1998). The evolution of stylometry in humanities scholarship. Literary and Linguistic Computing, 13(3), 111-117.
27. Hoover, D. (2002). Frequent word sequences and statistical stylistics. Literary and Linguistic Computing, 17, 157-180.
28. Kamps, J., Marx, M., Mokken, R.J., & Rijke, M. de. (2002). Words with attitude. In Proceedings of the 1st International Conference on Global WordNet. Mysore, India (pp. 332-341).
29. Karlgren, J. (2000). Stylistic experiments for information retrieval. Unpublished doctoral dissertation, SICS.
30. Kehagias, A., Petridis, V., Kaburlasos, V., & Fragkou, P. (2003). A comparison of word-and sense-based text categorization using several classification algorithms. Journal of Intelligent Information Systems, 21(3), 227-247.
31. Kessler, B., Nunberg, G., & Schütze, H. (1997). Automatic detection of text genre. In P.R. Cohen & W. Wahlster (Eds.), Proceedings of the 35th annual meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics (pp. 32-38). Somerset, NJ: Association for Computational Linguistics.
32. Kjell, B., & Frieder, O. (1992). Visualization of literary style. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (pp. 656-661). Chicago: IEEE Press.
33. Koppel, M., Akiva, N., & Dagan, I. (2003). A corpus-independent feature set for style-based text categorization. In Workshop on Computational Approaches to Style Analysis and Synthesis, 18th International Joint Conference on Artificial Intelligence.
34. Kushmerick, N. (1999). Learning to remove internet advertisement. In O. Etzioni, J.P. Müller, & J.M. Bradshaw (Eds.), Proceedings of the 3rd International Conference on Autonomous Agents (agents'99) (pp. 175-181). Seattle, WA: ACM Press.
35. Labov, W. (1973). Sociolinguistic patterns. Philadelphia: University of Pennsylvania Press.
36. Lewis, D., Schapire, R.E., Callan, J.P., & Papka, R. (1996). Training algorithms for linear text classifiers. In Proceedings of the 19th International Conference on Research and Development in Information Retrieval (pp. 298-306). New York: ACM Press.
37. Marcu, D. (1997). The rhetorical parsing of natural language texts. In Meeting of the Association for Computational Linguistics (pp. 96-103). Morristown, NJ: ACL.
38. Marcu, D. (1999). A decision-based approach to rhetorical parsing. In Proceedings of the ACL'99 (pp. 365-372). Morristown, NJ: ACL.
39. Martin, J.R., & White, P.R.R. (2005). The language of evaluation: Appraisal in English. London: Palgrave. http://www.grammatics.com/appraisal/
40. Matthews, R.A.J., & Merriam, T.V.N. (1997). Distinguishing literary styles using neural networks. In E. Fiesler & R. Beale (Eds.), Handbook of neural computation (pp. 8). New York: IOP Publishing and Oxford University Press.
41. Matthiessen, C. (1983). Systemic grammar in computation: The nigel case. In Proceedings of the Meeting of the European Association for Computational Linguistics (pp. 155-164). Morristown, NJ: ACL.
42. Matthiessen, C. (1995). Lexico-grammatical cartography: English systems. Tokyo: International Language Sciences.
43. Matthiessen, C., & Bateman, J.A. (1991). Text generation and systemic-functional linguistics: Experiences from English and Japanese. London, New York: Pinter, St. Martin's Press.
44. McCallum, A., Freitag, D., & Pereira, F. (2000). Maximum entropy Markov models for information extraction and segmentation. In Proceedings of the 17th International Conference on Machine Learning. Stanford, CA (pp. 591-598).
45. McEnery, A., & Oakes, M. (2000). Authorship studies/textual statistics. In R. Dale, H. Moisl, & H. Somers (Eds.), Handbook of natural language processing (pp. 234-248). Philadelphia: Dekker.
46. McKinney, V., Yoon, K., & Zahedi, F.M. (2002). The measurement of webcustomer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13(3), 296-315.
47. McMenamin, G. (2002). Forensic linguistics: Advances in forensic stylistics. Boca Raton, FL: CRC Press.
48. Miller, G., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. (1990). Wordnet: An on-line lexical database. International Journal of Lexicography, 3(4), 235-312.
49. Moore, J.D., & Pollack, M.E. (1992). Aproblem for RST: The need for multilevel discourse analysis. Computational Linguistics, 18(4), 537-544.
50. Mosteller, F., & Wallace, D.L. (1964). Inference and disputed authorship: The federalist. Reading, MA: Addison-Wesley.
51. Ng, V., & Cardie, C. (2002). Improving machine learning approaches to coreference resolution. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 104-111). Morristown, NJ: ACL.
52. O'Donnell, M. (1993). Reducing complexity in a systemic parser. In Proceedings of the 3rd International Workshop on Parsing Technologies. Tilburg, the Netherlands (pp. 10-13).
53. Osgood, C.E., Succi, G.J., & Tannenbaum, P.H. (1957). The measurement of meaning. Urbana: University of Illinois Press.
54. Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd ACL (pp. 271-278). Morristown, NJ: ACL.
55. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing (pp. 79-86). Morristown, NJ: ACL.
56. Patrick, J. (2004). The ScamSeek project: Text mining for finanical scams on the internet. In S. Simoff & G. Williams (Eds.), Proceedings of the 3rd Australasian Data Mining Conference (pp. 33-38).
57. Platt, J. (1998). Fast training of support vector machines using sequential minimal optimization. In B. Scholkopf, C.J.C. Burges, & A.J. Smola (Eds.), Advances in Kernel Methods - Support vector learning. Cambridge, MA, MIT Press.
58. Ponte, J.M., & Croft, W.B. (1998). A language modeling approach to information retrieval. In Proceedings of ACM SIGIR. New York: ACM Press.
59. Roth, D., & Yih, W. (2001). Relational learning via propositional algorithms: An information extraction case study. In Proceedings of the International Joint Conference on Artificial Intelligence (pp. 1257-1263).
60. Salton, G., & McGill, M. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.
61. Schauer, H., & Hahn, U. (2001). Anaphoric cues for coherence relations. In G. Angelova, K. Bontcheva, R. Mitkov, N. Nicolov, & N. Nikolov (Eds.), Proceedings of the Euroconference Recent Advances in Natural Language Processing (RANLP-2001) (pp. 228-234). Tzigov, Bulgaria.
62. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1-47.
63. Shakespeare, W. (n.d.). The complete Moby Shakespeare. http://www-tech.mit.edu/Shakespeare/
64. Stamatatos, E., Fakotakis, N., & Kokkinakis, G.K. (2000). Automatic text categorization in terms of genre, author. Computational Linguistics, 26(4), 471-495.
65. Taboada, M., & Grieve, J. (2004). Analyzing appraisal automatically. In AAAI Spring Symposium on Exploring Attitude and Affect in Text. Menlo Park, CA: AAAI Press.
66. Tang, R., Ng, K.B., Strzalkowski, T., & Kantor, P.B. (2003). Toward machine understanding of information quality. In Proceedings of Annual Meeting of American Society for Information Science and Technology (Vol. 40, pp. 213-220).
67. Teich, E. (1995). A proposal for dependency in systemic functional grammar - Metasemiosis in computational systemic functional linguistics. Unpublished doctoral dissertation, University of the Saarland and GMD/IPSI, Darmstadt, Germany.
68. Torvik, V.I., Weeber, M., Swanson, D.R., & Smalheiser, N.R. (2005). A probabilistic similarity metric for Medline records: A model for author name disambiguation. Journal of the American Society for Information Science and Technology, 56(2), 140-158.
69. Trudgill, P. (2001). Sociolinguistics: An introduction to language and society (4th ed.). New York: Penguin.
70. Tumey, P.D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting of me ACL (pp. 417-424). Morristown, NJ: ACL.
71. Tweedie, F., Singh, S., & Holmes, D. (1996). Neural network applications in stylometry: The Federalist Papers. Computers and the Humanities, 30(1), 1-10.
72. Whitelaw, C., Garg, N., & Argamon, S. (2005, May). Using appraisal taxonomies for sentiment analysis, In Proceedings of the 2nd Midwest Computational Linguistic Colloquium (MCLC 2005).
73. Wiebe, J., McKeever, K., & Bruce, R. (1998). Mapping collocational properties into machine learning features. In Proceedings of the 6th Workshop on Very Large Corpora (pp. 225-233), Morristown, NJ: ACL.
74. Wiebe, J., Wilson, T., & Bell, M. (2001). Identifying collocations for recognizing opinions. In Proceedings of ACL/EACL 2001 Workshop on Collocation (pp. 24-31).
75. Winograd, T. (1972). Understanding natural language. Orlando, FL: Academic Press.
76. Witten, I.H., & Frank, E. (2000). Data mining: Practical machine learning tools with Java implementations. San Francisco: Kaufmann.
77. Yule, G.U. (1944). Statistical study of literary vocabulary. Cambridge, UK: Cambridge University Press.