An extension of the Aspect PLSA Model to Active and Semi-supervised Learning for Text Classification
Anastasia Krithara(1), Massih-Reza Amini(3), Jean-Michel Renders(2), Cyril Goutte(3),
(1) NCSR Demokritos (2)Xerox Research Center Europe (3) CNRC
Patriarchou Grigoriou and Neapoleos 6, Chemin de Maupertuis 283, Bd Alexandre-Taché
Athens, Greece 38240 Meylan Gatineau, QC J8X 3X7
In this paper, we address the problem of learning aspect
models with partially labeled examples. We propose a method which
benefits from both semi-supervised and active learning frameworks. In
particular, we combine a semi-supervised extension of the PLSA algorithm with two active learning techniques. We perform experiments
over four different datasets and show the effectiveness of the combination
of the two frameworks.