1. SVM Based Learning System for F-term Patent Classification
Yaoyong Li, Kalina Bontcheva, Hamish Cunningham
Department of Computer Science
University of Sheffield

2. SVM for F-term Classification
Experiments with different contents of patent.
Adapt the SVM for F-term patent classification sub-task.
Difference between normal document classification and F-term patent classification.
Hierarchical SVM for the F-term taxonomy.
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3. Using Different Parts of Patent
Results using different contents of patent.
A-Precision
R-Precision
F-measure
Abstract only
0.4279
0.3908
0.3647
Full text of patent
0.4688
0.4270
0.3998
Full text + F-term description
0.4779
0.4363
0.4125
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4. Evaluation of F-term classification
Document classification, based on category
Learn a classifier for one category, and evaluate it on new documents.
F-term patent classification: based on patent
Compare the scores of all classifiers for one patent.
So need normalise the scores of different classifiers for fair comparison.
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5. Benefit of Normalisation
Comparison between the results with and without normalisation.
A-Precision
R-Precision
F-measure
Without normalisation
0.4643
0.4330
0.3677
With normalisation
0.4779
0.4363
0.4125
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6. Hierarchical SVM Flat SVM Hierarchical SVM for taxonomy
Learn an SVM classifier for one class using the one vs. all others approach.
Ignore the relations between classes.
Hierarchical SVM for taxonomy
Learn an SVM classifier for one class by using only the training examples which are the positive examples of the parent class.
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7. Comparison of H-SVM and Flat SVM
Using the conventional measure.
A-Precision
R-Precision
F-measure
Flat SVM
0.4779
0.4363
0.4125
H-SVM
0.2376
0.2164
0.2257
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8. Comparison of H-SVM and Flat SVM
Using the measures taking into account the relations between F-terms.
A-Precision
R-Precision
F-measure
Flat SVM
0.6269
0.6194
0.4429
H-SVM
0.5193
0.5414
0.3605
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9. Possible Reasons of Failure of H-SVM
F-term classification is a multi-label problem,
One instance may have more than one true class.
But the H-SVM we used was designed for the case that each instance has only one true class.
F-terms under a given theme are not hierarchically related with each other in the strict sense.
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10. Conclusions
Adapted successfully the SVM for the F-term patent classification subtask.
Demonstrated that more information are helpful.
Analysis of the failure of H-SVM
How to adapt the H-SVM?
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