  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.
