Hot topic detection and technology trend tracking for patents utilizing term frequency and proportional document frequency and semantic information

摘要

This paper proposes a methodology for identifying hot topics and tracking technology trends from the patent domain. The methodology uses frequency information in combination with the International Patent Classification (IPC) to capture semantic information on word categorization, doing so in a way that heretofore has not been employed for topic detection and trend tracking. Term Frequency and Proportional Document Frequency (TF*PDF) is employed as a means to detect hot topics from patents, and IPCs are used to calculate semantic importance of terms based on the IPCs where terms are distributed. Aging Theory is also used to calculate the variation of trends over time. Four types of trends including very stable trends, stable trends, normal trends, and unstable trends are defined and evaluated based on TF*PDF and TF*PDF combined with Aging Theory. Experiment results show that for very stable trends, the combination of TF*PDF and Aging Theory achieves 0.976% in Precision; for stable trends and all trends, TF*PDF achieves 0.959% and 0.84% in Precision, respectively. By applying TF*PDF in consideration of semantic information, we also show a new criteria for weighting hot topics and technology trend tracking.