An evaluation of text classification methods for literary study
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[1] Hinrich Schütze,et al. Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.
[2] R. Macksey,et al. Museum of Words: The Poetics of Ekphrasis from Homer to Ashbery , 1995 .
[3] Patrick Juola,et al. Authorship Attribution , 2008, Found. Trends Inf. Retr..
[4] David D. Lewis,et al. An evaluation of phrasal and clustered representations on a text categorization task , 1992, SIGIR '92.
[5] James H. Martin,et al. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .
[6] Andrew McCallum,et al. Distributional clustering of words for text classification , 1998, SIGIR '98.
[7] Jussi Karlgren,et al. Recognizing Text Genres With Simple Metrics Using Discriminant Analysis , 1994, COLING.
[8] F. Mosteller,et al. Inference and Disputed Authorship: The Federalist , 1966 .
[9] D. Altman,et al. Multiple significance tests: the Bonferroni method , 1995, BMJ.
[10] Graeme Hirst,et al. Collocations as Cues to Semantic Orientation , 2004 .
[11] Santosh S. Vempala,et al. Latent semantic indexing: a probabilistic analysis , 1998, PODS '98.
[12] Concept tree based clustering visualization with shaded similarity matrices , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[13] David M. Pennock,et al. Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.
[14] Jean Guy Meunier,et al. Categorisation Techniques in Computer-Assisted Reading and Analysis of Texts (CARAT) in the Humanities , 2003, Comput. Humanit..
[15] Rohini K. Srihari,et al. Using Verbs and Adjectives to Automatically Classify Blog Sentiment , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.
[16] Thorsten Joachims,et al. Learning to classify text using support vector machines - methods, theory and algorithms , 2002, The Kluwer international series in engineering and computer science.
[17] Michael Gamon,et al. Linguistic correlates of style: authorship classification with deep linguistic analysis features , 2004, COLING.
[18] Tong Zhang,et al. Text Mining: Predictive Methods for Analyzing Unstructured Information , 2004 .
[19] Harald Krottmaier. The Future of Digital Libraries , 2004 .
[20] Ian H. Witten,et al. Text mining in a digital library , 2004, International Journal on Digital Libraries.
[21] Stephen Ramsay,et al. In Praise of Pattern , 2005 .
[22] Martin F. Porter,et al. An algorithm for suffix stripping , 1997, Program.
[23] Tandy J. Warnow,et al. Analyzing the Order of Items in Manuscripts of The Canterbury Tales , 2003, Computers and the Humanities.
[24] Michael L. Littman,et al. Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.
[25] Efstathios Stamatatos,et al. Text Genre Detection Using Common Word Frequencies , 2000, COLING.
[26] George Forman,et al. An Extensive Empirical Study of Feature Selection Metrics for Text Classification , 2003, J. Mach. Learn. Res..
[27] Ellen Riloff,et al. Little words can make a big difference for text classification , 1995, SIGIR '95.
[28] Bei Yu,et al. Sentence recall game: a novel tool for collecting data to discover language usage patterns , 2010, HCOMP '10.
[29] Dunja Mladenic,et al. Feature Selection for Unbalanced Class Distribution and Naive Bayes , 1999, ICML.
[30] David D. Lewis,et al. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.
[31] Geoffrey Rockwell,et al. What is Text Analysis, Really? , 2003, Lit. Linguistic Comput..
[32] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[33] Ellen Riloff,et al. Learning Extraction Patterns for Subjective Expressions , 2003, EMNLP.
[34] Ramakrishnan Srikant,et al. Mining newsgroups using networks arising from social behavior , 2003, WWW '03.
[35] Shlomo Argamon,et al. Toward meaningful computing , 2006, CACM.
[36] Yoram Singer,et al. Context-sensitive learning methods for text categorization , 1996, SIGIR '96.
[37] Ron Kohavi,et al. Irrelevant Features and the Subset Selection Problem , 1994, ICML.
[38] Jerome McGann,et al. Radiant Textuality: Literature after the World Wide Web , 2001 .
[39] Bei Yu,et al. A Longitudinal Study of Language and Ideology in Congress , 2010 .
[40] Jörg Kindermann,et al. Text Categorization with Support Vector Machines. How to Represent Texts in Input Space? , 2002, Machine Learning.
[41] Shlomo Argamon,et al. Automatically Categorizing Written Texts by Author Gender , 2002, Lit. Linguistic Comput..
[42] Mark Olsen,et al. Mining Eighteenth Century Ontologies: Machine Learning and Knowledge Classification in the Encyclopédie , 2009, Digit. Humanit. Q..
[43] Aidan Finn,et al. Learning to classify documents according to genre , 2006, J. Assoc. Inf. Sci. Technol..
[44] Douglas Biber,et al. Dimensions of Register Variation , 1995 .
[45] Catherine Plaisant,et al. Exploring erotics in Emily Dickinson's correspondence with text mining and visual interfaces , 2006, Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '06).
[46] Bei Yu,et al. Genre-Based In-Document Content Type Classification , 2003 .
[47] James H. Martin,et al. Speech and language processing: an introduction to natural language processing , 2000 .
[48] Stan Matwin,et al. Feature Engineering for Text Classification , 1999, ICML.
[49] Stefan Kaufmann,et al. Classifying Party Affiliation from Political Speech , 2008 .
[50] Hugh Craig. Authorial attribution and computational stylistics: if you can tell authors apart, have you learned anything about them? , 1999 .
[51] Janyce Wiebe,et al. Learning Subjective Adjectives from Corpora , 2000, AAAI/IAAI.
[52] Patrick Juola,et al. A Controlled-corpus Experiment in Authorship Identification by Cross-entropy , 2003 .
[53] David I. Holmes,et al. Neural network applications in stylometry: The Federalist Papers , 1996, Comput. Humanit..
[54] Yiming Yang,et al. A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.
[55] Fazli Can,et al. Change of Writing Style with Time , 2004, Comput. Humanit..
[56] D. Holmes. The Analysis of Literary Style — a Review , 1985 .
[57] Yiming Yang,et al. An Evaluation of Statistical Approaches to Text Categorization , 1999, Information Retrieval.
[58] Susan T. Dumais,et al. Inductive learning algorithms and representations for text categorization , 1998, CIKM '98.
[59] Jörg Kindermann,et al. Authorship Attribution with Support Vector Machines , 2003, Applied Intelligence.
[60] Ido Dagan,et al. A Corpus-Independent Feature Set for Style-Based Text Categorization , 2003 .
[61] David A. Hull. Stemming algorithms: a case study for detailed evaluation , 1996 .
[62] Kenneth Ward Church. One term or two? , 1995, SIGIR '95.
[63] Bei Yu,et al. Building Folk UMLS: An Approach to Finding Meaning of Folk Terms in Medical Domain , 2010 .
[64] Janyce Wiebe,et al. Effects of Adjective Orientation and Gradability on Sentence Subjectivity , 2000, COLING.
[65] Andrew McCallum,et al. A comparison of event models for naive bayes text classification , 1998, AAAI 1998.
[66] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[67] Christopher J. C. Burges,et al. A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.
[68] Marko Grobelnik,et al. Feature selection using linear classifier weights: interaction with classification models , 2004, SIGIR '04.
[69] V. Vapnik. Estimation of Dependences Based on Empirical Data , 2006 .
[70] Stephen Ramsay. Toward an Algorithmic Criticism , 2003 .
[71] T C Mendenhall,et al. THE CHARACTERISTIC CURVES OF COMPOSITION. , 1887, Science.
[72] Pedro M. Domingos,et al. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.
[73] Lina Zhou,et al. Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.
[74] Bei Yu,et al. Exploring the characteristics of opinion expressions for political opinion classification , 2008, DG.O.
[75] David D. Lewis,et al. Feature Selection and Feature Extraction for Text Categorization , 1992, HLT.
[76] John D. McGregor,et al. Getting there from here: a roadmap for software product line adoption , 2006, CACM.
[77] David Ellis,et al. The English literature researcher in the age of the Internet , 2005, J. Inf. Sci..
[78] Bo Pang,et al. Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.
[79] John Unsworth,et al. Toward Discovering Potential Data Mining Applications in Literary Criticism , 2006 .
[80] Fabrizio Sebastiani,et al. Machine learning in automated text categorization , 2001, CSUR.
[81] Susan Brewer,et al. Information storage and retrieval , 1959, ACM '59.
[82] Peter D. Turney. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.
[83] Ricardo Baeza-Yates,et al. Information Retrieval: Data Structures and Algorithms , 1992 .
[84] Richard T. Watson,et al. The Internet and the birth of real consumer power , 2002 .
[85] Fionn Murtagh,et al. A Survey of Recent Advances in Hierarchical Clustering Algorithms , 1983, Comput. J..
[86] Dawn Archer,et al. Love - 'a familiar or a devil'? An Exploration of Key Domains in Shakespeare's Comedies and Tragedies , 2009 .
[87] Thorsten Joachims,et al. Text categorization with support vector machines , 1999 .
[88] Jiawei Han,et al. Data mining via support vector machines: scalability, applicability, and interpretability , 2004 .
[89] Jonathon Read,et al. Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification , 2005, ACL.
[90] D. Holmes. The Evolution of Stylometry in Humanities Scholarship , 1998 .
[91] Ray Siemens,et al. A companion to digital literary studies , 2007 .
[92] Eric Brill,et al. Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging , 1995, CL.
[93] Hinrich Schütze,et al. Automatic Detection of Text Genre , 1997, ACL.
[94] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[95] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[96] Bei Yu,et al. Collecting legacy corpora from social science research for text mining evaluation , 2010, ASIST.
[97] Bei Yu,et al. Strangeness-based feature weighting and classification of gene expression profiles , 2008, SAC '08.
[98] Ran El-Yaniv,et al. Distributional Word Clusters vs. Words for Text Categorization , 2003, J. Mach. Learn. Res..
[99] I.N. Bozkurt,et al. Authorship attribution , 2007, 2007 22nd international symposium on computer and information sciences.