Automatisk genreklassifikation : en experimentell studie

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This thesis aims at examining to what extent a few, algorithmically very easily extractable document features can be used to classify electronic documents according to genre. A set of experiments is therefore carried out, using only 11 such simple features in an attempt to classify 84 documents belonging to electronic academic journals into three manually identified genres: table of contents, article, and review. The 11 features are also divided into three sets, containing metrics of words and sentences; punctuation marks; and URL links, respectively. The performance when using these sets of features is then measured with regard to classification accuracy, using a k-NN classifier, four different values of k (1, 3, 5, 7), and both leave-one-out and 10-fold cross-validation. Best results are achieved when using all three feature sets (i.e. all 11 features) and k=3, with an overall accuracy of 96% (81 of the 84 documents correctly classified), regardless of method for cross-validation. These results are significantly better than those of a referential baseline, conceived as the case where all instances would be guessed as belonging to the most populated class, with a corresponding accuracy of 49%. While not considered as disappointing in any way, the results are viewed by the author as perhaps an expression of a somewhat easy classification task. He therefore concludes by advocating further research on the capability of very simple featur...