Anzeige
Erschienen in:
2006 | OriginalPaper | Buchkapitel
Segmented Document Classification: Problem and Solution
verfasst von : Hang Guo, Lizhu Zhou
Erschienen in: Database and Expert Systems Applications
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In recent years, structured text documents like XML files are playing an important role in the Web-based applications. Among them, there are some documents that are segmented into different sections like “title”,“body”, etc. We call them “segmented documents”. To classify segmented documents, we can treat them as bags of words and use well-developed text classification models. However different sections in a segmented document may have different impact on the classification result. It is better to treat them differently in the classification process. Following this idea, two algorithms: IN_MIX and OUT_MIX are designed to label segmented documents by a trained classifier. We perform our algorithms using four frequently used models: SVM, NaiveBayes, Regression and Instance-based Classifiers. According to the experiment on Reuters-21578, the performance of different classification models is improved comparing to the conventional bag of words method.