F-term classification Experiments at NTCIR-6 for Justsytems
Masaki Rikitoku · NTCIR · 2007
We conducted the classification subtask at NTCIR6 Patent Retrieval Task using a system based on three document classifiers, namely, a one-vs-rest SVM classifier, multi-topic classifier, and binary Naive Bayes classifier. The multi-topic classifier was constructed on the basis of the maximum margin principle and applied to multiple F-term classification. From the experimental results, this multi-topic classifier yielded a higher F1 value than the one-vs-rest SVM in many cases. In addition, we employed the one-vs-rest SVM classifier. The SVM classifier has certain drawbacks such as low recall performance and large learning time. In order to solve these problems, we used heuristics for achieving random reduction of a part of the negative examples and division of learning. These procedures lead to a reduction in learning time and improve the classification performance when appropriate parameters are set.