Comparing and Combining Two Approaches to Automated Subject Classification of Text

• Published in: Lecture notes in computer science pp. 467 - 470
• Main Authors: Golub, Koraljka, Ardö, Anders, Mladenić, Dunja, Grobelnik, Marko
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Biblioteks- och informationsvetenskap; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; Information systems. Data bases; Library and Information Science; Memory organisation. Data processing; Software; Speech and sound recognition and synthesis. Linguistics; Vocabulary; Automatic classification; String matching; Database; Vector support machine; Information retrieval; Natural language; Text; Electronic library; Artificial intelligence; Pattern matching; Content analysis
• ISBN: 9783540446361; 3540446362; 3540446389; 9783540446385
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11863878_45

A machine-learning and a string-matching approach to automated subject classification of text were compared, as to their performance, advantages and downsides. The former approach was based on an SVM algorithm, while the latter comprised string-matching between a controlled vocabulary and words in the text to be classified. Data collection consisted of a subset from Compendex, classified into six different classes. It was shown that SVM on average outperforms the string-matching approach: our hypothesis that SVM yields better recall and string-matching better precision was confirmed only on one of the classes. The two approaches being complementary, we investigated different combinations of the two based on combining their vocabularies. The results have shown that the original approaches, i.e. machine-learning approach without using background knowledge from the controlled vocabulary, and string-matching approach based on controlled vocabulary, outperform approaches in which combinations of automatically and manually obtained terms were used. Reasons for these results need further investigation, including a larger data collection and combining the two using predictions.