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

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 t...