Research on Automated Patent Map Construction

在本研究中，建立了自动构建专利地图的技术。提出了一种基于卡方统计量的术语加权分类方法，并在ntcirr -6专利检索任务的分类子任务中进行了评价。在这项任务中，大量的专利申请被划分为f项类别。因此，专利分类系统要求分类速度快，分类精度高。卡方统计量可以计算出单词在f项中出现的频率和单词在f项中不出现的频率。所提出的方法将单词视为标量值，排序算法只是将测试专利文档中包含的每个单词的单词值添加到每个F-term中。因此，该方法提供的分类速度明显快于其他方法。对该方法进行了a精度、r精度和f测量。虽然本文提出的方法没有获得最好的分数，但该方法的分类精度与其他使用机器学习或向量分类方法的方法一样高。在NTCIR6评估任务中，不评估处理速度。因此，处理速度是我自己评估的。评价结果表明，该方法比使用向量分类方法的速度要快得多。分类精度和处理速度的评价结果表明，该方法是有效的，具有实用性。

In this research, technologies for automated patent map construction are established.A term weighting classification method using the chi-square statistic is proposed and evaluated in the classification subtask at NTCIR-6 patent retrieval task. In this task, large numbers of patent applications are classified into F-term categories. Therefore, a patent classification system requires high classification speed, as well as high classification accuracy. The chi-square statistic can calculate the frequency of word appearance in the F-term and the frequency of word non-appearance in the F-term. The proposed method treats words as a scalar value and a ranking algorithm simply adds the word values of each word included in the test patent document in each F-term. Therefore, the proposed method provides classification that is significantly faster than other methods.The proposed method is evaluated in A-precision, R-precision, and F-measure. Although the proposed method did not obtain the best score, this method achieves a classification accuracy that is as high as those of other methods using machine learning or the vector classification method. In the NTCIR6 evaluation task, the processing speed is not evaluated. Therefore processing speed is evaluated on my own accord. The evaluation results show that the proposed method is much faster than that using the vector classification method.Evaluation results of classification accuracy and processing speed show that the proposed method is confirmed to be effective and to be practical.