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Text Classification: A Sequential Reading Approach

Gabriel Dulac-Arnold 1 Ludovic Denoyer 1 Patrick Gallinari 1 
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
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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Submitted on : Friday, July 8, 2011 - 9:45:57 AM
Last modification on : Wednesday, January 12, 2022 - 3:47:22 AM

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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. pp.411-423, ⟨10.1007/978-3-642-20161-5_41⟩. ⟨inria-00607185⟩



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