Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian Belief Networks as a tool for stochastic parsing
Speech Communication
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Statistical Language Learning
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Evaluating OCR and Non-OCR Text Representations for Learning Document Classifiers
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Making of America: Online Searching and Page Presentation at theUniversity of Michigan
Making of America: Online Searching and Page Presentation at theUniversity of Michigan
A New Probabilistic Model of Text Classification and Retrieval TITLE2:
A New Probabilistic Model of Text Classification and Retrieval TITLE2:
Searching for experts on the Web: A review of contemporary expertise locator systems
ACM Transactions on Internet Technology (TOIT)
Resume information extraction with cascaded hybrid model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A class-feature-centroid classifier for text categorization
Proceedings of the 18th international conference on World wide web
Tree-Based Method for Classifying Websites Using Extended Hidden Markov Models
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An impact of linguistic features on automated classification of OCR texts
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A subspace decision cluster classifier for text classification
Expert Systems with Applications: An International Journal
Intelligent search on the internet
Reasoning, Action and Interaction in AI Theories and Systems
The impact of OCR accuracy and feature transformation on automatic text classification
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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Text categorization is typically formulated as a concept learning prob lem where each instance is a single isolated document. In this paper we are interested in a more general formulation where documents are organized as page sequences, as naturally occurring in digital libraries of scanned books and magazines. We describe a method for classifying pages of sequential OCR text documents into one of several assigned categories and suggest that taking into account contextual information provided by the whole page sequence can significantly improve classification accuracy. The proposed architecture relies on hidden Markov models whose emissions are bag-of-words according to a multinomial word event model, as in the generative portion of the Naive Bayes classifier. Our results on a collection of scanned journals from the Making of America project confirm the importance of using whole page sequences. Empirical evaluation indicates that the error rate (as obtained by running a plain Naive Bayes classifier on isolated page) can be roughly reduced by half if contextual information is incorporated.