本文選題：專利文獻 + LDA��； 參考：《現(xiàn)代情報》2017年05期

【摘要】：[目的 /意義]運用概率主題模型全面研究專利文獻主題演化,分析專利技術(shù)發(fā)展過程及趨勢。[方法/過程]LDA模型按時間窗口對專利文本建模,困惑度確定最優(yōu)主題數(shù),按專利文本結(jié)構(gòu)特性提取主題向量,采用JS散度度量主題之間的關(guān)聯(lián),引入IPC分類號度量技術(shù)主題強度,最后實現(xiàn)主題強度、主題內(nèi)容和技術(shù)主題強度3方面的演化研究。[結(jié)果 /結(jié)論]實驗結(jié)果表明:該方法能夠深入挖掘?qū)＠墨I的主題,可以較好地分析專利技術(shù)隨時間的演化規(guī)律,幫助相關(guān)從業(yè)人員了解專利技術(shù)的演化過程及趨勢。
[Abstract]:Objective / significance: to study the topic evolution of patent literature and analyze the development process and trend of patent technology by using probabilistic subject model. [method / process] LDA model models patent texts according to time window, determines the optimal number of topics by confusion, extracts topic vectors according to the structural characteristics of patent texts, and measures the correlation of topics by JS divergence. This paper introduces IPC classification number to measure the technical topic strength, and finally realizes the evolution research of theme intensity, theme content and technical theme intensity. [results / conclusion] the experimental results show that this method can dig out the subject of patent documents, analyze the evolution law of patent technology over time, and help relevant practitioners to understand the evolution process and trend of patent technology.
【作者單位】： 江西理工大學(xué)信息工程學(xué)院;
【基金】：國家自然科學(xué)基金項目“創(chuàng)新網(wǎng)絡(luò)異質(zhì)性與企業(yè)創(chuàng)新績效關(guān)系研究”(項目編號:71462018) 江西省研究生創(chuàng)新專項基金資助項目“基于領(lǐng)域知識的LDA主題模型”(項目編號:YC2015-S304)
【分類號】：G255.53
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本文編號：1938319