Text Classification: A Sequential Reading Approach

Abstract : 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.
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Communication dans un congrès
33rd European Conference on Information Retrieval (ECIR 2011), Apr 2011, Dublin, Ireland. Springer Berlin / Heidelberg, 6611, pp.411-423, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-20161-5_41〉
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https://hal.inria.fr/inria-00607185
Contributeur : Gabriel Dulac-Arnold <>
Soumis le : vendredi 8 juillet 2011 - 09:45:57
Dernière modification le : jeudi 11 janvier 2018 - 06:27:09

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Gabriel Dulac-Arnold, Ludovic Denoyer, Patrick Gallinari. Text Classification: A Sequential Reading Approach. 33rd European Conference on Information Retrieval (ECIR 2011), Apr 2011, Dublin, Ireland. Springer Berlin / Heidelberg, 6611, pp.411-423, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-20161-5_41〉. 〈inria-00607185〉

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