Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Efficient passage ranking for document databases
ACM Transactions on Information Systems (TOIS)
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Constraint Classification: A New Approach to Multiclass Classification
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Text classification using string kernels
The Journal of Machine Learning Research
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Extraction of coherent relevant passages using hidden Markov models
ACM Transactions on Information Systems (TOIS)
Utilizing passage-based language models for document retrieval
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
A comparison study on multiple binary-class SVM methods for unilabel text categorization
Pattern Recognition Letters
Datum-wise classification: a sequential approach to sparsity
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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We propose to model the text classification process as a sequential decision process. In this process, an agent learns to classify documents into topics while reading the document sentences sequentially and learns to stop as soon as enough information was read for deciding. The proposed algorithm is based on a modelisation of Text Classification as a Markov Decision Process and learns by using Reinforcement Learning. Experiments on four different classical mono-label corpora show that the proposed approach performs comparably to classical SVM approaches for large training sets, and better for small training sets. In addition, the model automatically adapts its reading process to the quantity of training information provided.