PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Text Classification: A Sequential Reading Approach
Gabriel Dulac Arnold, Ludovic Denoyer and Patrick Gallinari
In: ECIR 2011(2011).

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:9284
Deposited By:Ludovic Denoyer
Deposited On:21 February 2012