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Title: Using graph-kernels to represent semantic information in text classification
Authors: Gonçalves, Teresa
Quaresma, Paulo
Keywords: graph-kernels
text classification
machine learning
Issue Date: Jul-2009
Publisher: Springer-Verlag
Abstract: Most text classification systems use bag-of-words represen- tation of documents to find the classification target function. Linguistic structures such as morphology, syntax and semantic are completely ne- glected in the learning process. This paper proposes a new document representation that, while includ- ing its context independent sentence meaning, is able to be used by a structured kernel function, namely the direct product kernel. The proposal is evaluated using a dataset of articles from a Portuguese daily newspaper and classifiers are built using the SVM algorithm. The results show that this structured representation, while only partially de- scribing document’s significance has the same discriminative power over classes as the traditional bag-of-words approach.
Type: article
Appears in Collections:INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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