Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Integrating Background Knowledge into Nearest-Neighbor Text Classification
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Authorship Attribution with Support Vector Machines
Applied Intelligence
Augmenting Naive Bayes Classifiers with Statistical Language Models
Information Retrieval
Authorship attribution using word sequences
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Effective and scalable authorship attribution using function words
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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As any other text categorization task, authorship attribution requires a large number of training examples. These examples, which are easily obtained for most of the tasks, are particularly difficult to obtain for this case. Based on this fact, in this paper we investigate the possibility of using Web-based text mining methods for the identification of the author of a given poem. In particular, we propose a semi-supervised method that is specially suited to work with justfew training examples in order to tackle the problem of the lack of data with the same writing style. The method considers the automatic extraction of the unlabeled examples from the Web and its iterative integration into the training data set. To the knowledge of the authors, a semi-supervised method which makes use of the Web as support lexical resource has not been previously employed in this task. The results obtained on poem categorization show that this method may improve the classification accuracy and it is appropriate to handle the attribution of short documents.