Comparing and Combining Two Approaches to Automated Subject Classification of Text Export

@incollection{citeulike:1429587, abstract = {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.}, author = {Golub, Koraljka and Ard\"{o}, Anders and Mladeni\'{c}, Dunja and Grobelnik, Marko}, citeulike-article-id = {1429587}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11863878_45}, doi = {10.1007/11863878_45}, journal = {Research and Advanced Technology for Digital Libraries}, keywords = {classification--automatic, classification--text--automated, controlled\_vocabularies, string\_matching, support\_vector\_machines}, pages = {467--470}, posted-at = {2007-07-02 23:33:10}, priority = {3}, title = {Comparing and Combining Two Approaches to Automated Subject Classification of Text}, url = {http://dx.doi.org/10.1007/11863878_45}, year = {2006} }

TY - CHAP ID - citeulike:1429587 L3 - citeulike-article-id:1429587 N2 - 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. JF - Research and Advanced Technology for Digital Libraries EP - 470 TI - Comparing and Combining Two Approaches to Automated Subject Classification of Text SP - 467 KW - classification--automatic KW - classification--text--automated KW - controlled_vocabularies KW - string_matching KW - support_vector_machines AU - Golub, Koraljka AU - Ardö, Anders AU - Mladenić, Dunja AU - Grobelnik, Marko PY - 2006/// UR - http://dx.doi.org/10.1007/11863878_45 ER -