It is well known that supervised text classification methods need to learn from many labeled examples to achieve a high accuracy. However, in a real context, sufficient labeled examples are not always available. In this paper we demonstrate that a way to obtain a high accuracy, when the number of labeled examples is low, is to consider structured features instead of list of weighted words as observed features. The proposed vector of features considers a hierarchical structure, named a mixed Graph of Terms, composed of a directed and an undirected sub-graph of words, that can be automatically constructed from a set of documents through the probabilistic Topic Model. © Springer-Verlag Berlin Heidelberg 2013.
Colace, F., De Santo, M., Greco, L., Napoletano, P. (2013). Learning to Classify Text Using a Few Labeled Examples. In Communications in Computer and Information Science (pp. 200-214). Springer Verlag [10.1007/978-3-642-37186-8_13].
Learning to Classify Text Using a Few Labeled Examples
NAPOLETANO, PAOLO
2013
Abstract
It is well known that supervised text classification methods need to learn from many labeled examples to achieve a high accuracy. However, in a real context, sufficient labeled examples are not always available. In this paper we demonstrate that a way to obtain a high accuracy, when the number of labeled examples is low, is to consider structured features instead of list of weighted words as observed features. The proposed vector of features considers a hierarchical structure, named a mixed Graph of Terms, composed of a directed and an undirected sub-graph of words, that can be automatically constructed from a set of documents through the probabilistic Topic Model. © Springer-Verlag Berlin Heidelberg 2013.
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