| Project/Area Number |
17500063
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Media informatics/Database
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| Research Institution | Nagaoka University of Technology. |
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Principal Investigator |
YUKAWA Takashi Nagaoka University of Technology, Engineering School, Associate Professor, 工学部, 助教授 (70345536)
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2006: ¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 2005: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | Patent classification / F-term classification / Patent map / Term weighting / Web application / Information retrieval / F-TERM / 概念ベース / ベクトル空間モデル |
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| Research Abstract |
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.
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