A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation
Tekijät: Pahikkala T, Ginter F, Boberg J, Jarvinen J, Salakoski T
Kustantaja: BMC
Julkaisuvuosi: 2005
Journal: BMC Bioinformatics
Tietokannassa oleva lehden nimi: BMC BIOINFORMATICS
Lehden akronyymi: BMC BIOINFORMATICS
Artikkelin numero: ARTN 157
Vuosikerta: 6
Sivujen määrä: 12
ISSN: 1471-2105
DOI: https://doi.org/10.1186/1471-2105-6-157
Tiivistelmä
Background: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very efficient in general data mining tasks. We explore their capability for the gene versus protein name disambiguation task.Results: We incorporated into the conventional SVM a weighting scheme based on distances of context words from the word to be disambiguated. This weighting scheme increased the performance of SVMs by five percentage points giving performance better than 85% as measured by the area under ROC curve and outperformed the Weighted Additive Classifier, which also incorporates the weighting, and the Naive Bayes classifier.Conclusion: We show that the performance of SVMs can be improved by the proposed weighting scheme. Furthermore, our results suggest that in this study the increase of the classification performance due to the weighting is greater than that obtained by selecting the underlying classifier or the kernel part of the SVM.
Background: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very efficient in general data mining tasks. We explore their capability for the gene versus protein name disambiguation task.Results: We incorporated into the conventional SVM a weighting scheme based on distances of context words from the word to be disambiguated. This weighting scheme increased the performance of SVMs by five percentage points giving performance better than 85% as measured by the area under ROC curve and outperformed the Weighted Additive Classifier, which also incorporates the weighting, and the Naive Bayes classifier.Conclusion: We show that the performance of SVMs can be improved by the proposed weighting scheme. Furthermore, our results suggest that in this study the increase of the classification performance due to the weighting is greater than that obtained by selecting the underlying classifier or the kernel part of the SVM.
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