The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Recognizing text genres with simple metrics using discriminant analysis
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Journal of the American Society for Information Science and Technology
Multiple sets of features for automatic genre classification of web documents
Information Processing and Management: an International Journal
Techniques for improving the performance of naive bayes for text classification
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Cuisine: Classification using stylistic feature sets and-or name-based feature sets
Journal of the American Society for Information Science and Technology
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Text classification is an important and challenging research domain. In this paper, identifying historical period and ethnic origin of documents using stylistic feature sets is investigated. The application domain is Jewish Law articles written in Hebrew-Aramaic. Such documents present various interesting problems for stylistic classification. Firstly, these documents include words from both languages. Secondly, Hebrew and Aramaic are richer than English in their morphology forms. The classification is done using six different sets of stylistic features: quantitative features, orthographic features, topographic features, lexical features and vocabulary richness. Each set of features includes various baseline features, some of them formalized by us. SVM has been chosen as the applied machine learning method since it has been very successful in text classification. The quantitative set was found as very successful and superior to all other sets. Its features are domain-independent and language-independent. It will be interesting to apply these feature sets in general and the quantitative set in particular into other domains as well as into other.