2013 | OriginalPaper | Buchkapitel
Learning to Classify Text Using a Few Labeled Examples
verfasst von : Francesco Colace, Massimo De Santo, Luca Greco, Paolo Napoletano
Erschienen in: Knowledge Discovery, Knowledge Engineering and Knowledge Management
Verlag: Springer Berlin Heidelberg
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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.