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In this paper it is presented a model of text representation based on graphs. The model is applied in the particular case study of authorship attribution. The experiments were performed by using a corpus made up of 500 documents written by 10 different authors (50 documents per author). The obtained results highlight the benefit of using text features at different levels of language description in tasks associated to automatic processing of information. In particular, we have obtained a performance of 57% of accuracy for the authorship attribution task.

Automatic Linguistic Pattern Identification Based on Graph Text Representation