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Comparing and combining two approaches to automated subject classification of text

Golub, Koraljka LU ; Ardö, Anders LU ; Mladenic, Dunja and Grobelnik, Marko (2006) 10th European Conference, ECDL 2006 4172. p.467-470
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.... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Research and Advanced Technology for Digital Libraries. Proceedings / Lecture Notes in Computer Science
volume
4172
pages
467 - 470
publisher
Springer
conference name
10th European Conference, ECDL 2006
conference location
Alicante, Spain
conference dates
2006-09-17 - 2006-09-22
external identifiers
  • wos:000241101500045
  • scopus:33750236672
ISSN
1611-3349
0302-9743
DOI
10.1007/11863878_45
language
English
LU publication?
yes
id
2bb00c04-3a65-4f21-8708-615f60bdc107 (old id 387253)
alternative location
http://www.eit.lth.se/fileadmin/eit/home/hs.aar/Publ/ECDL2006.pdf
date added to LUP
2016-04-01 12:00:35
date last changed
2021-06-30 04:10:46
@inproceedings{2bb00c04-3a65-4f21-8708-615f60bdc107,
  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 Ardö, Anders and Mladenic, Dunja and Grobelnik, Marko},
  booktitle    = {Research and Advanced Technology for Digital Libraries. Proceedings / Lecture Notes in Computer Science},
  issn         = {1611-3349},
  language     = {eng},
  pages        = {467--470},
  publisher    = {Springer},
  title        = {Comparing and combining two approaches to automated subject classification of text},
  url          = {http://dx.doi.org/10.1007/11863878_45},
  doi          = {10.1007/11863878_45},
  volume       = {4172},
  year         = {2006},
}