本文選題：專(zhuān)利文獻(xiàn) + LDA�。� 參考：《現(xiàn)代情報(bào)》2017年05期

【摘要】：[目的 /意義]運(yùn)用概率主題模型全面研究專(zhuān)利文獻(xiàn)主題演化,分析專(zhuān)利技術(shù)發(fā)展過(guò)程及趨勢(shì)。[方法/過(guò)程]LDA模型按時(shí)間窗口對(duì)專(zhuān)利文本建模,困惑度確定最優(yōu)主題數(shù),按專(zhuān)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(lèi)號(hào)度量技術(shù)主題強(qiáng)度,最后實(shí)現(xiàn)主題強(qiáng)度、主題內(nèi)容和技術(shù)主題強(qiáng)度3方面的演化研究。[結(jié)果 /結(jié)論]實(shí)驗(yàn)結(jié)果表明:該方法能夠深入挖掘?qū)＠墨I(xiàn)的主題,可以較好地分析專(zhuān)利技術(shù)隨時(shí)間的演化規(guī)律,幫助相關(guān)從業(yè)人員了解專(zhuān)利技術(shù)的演化過(guò)程及趨勢(shì)。
[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(guó)家自然科學(xué)基金項(xiàng)目“創(chuàng)新網(wǎng)絡(luò)異質(zhì)性與企業(yè)創(chuàng)新績(jī)效關(guān)系研究”(項(xiàng)目編號(hào):71462018) 江西省研究生創(chuàng)新專(zhuān)項(xiàng)基金資助項(xiàng)目“基于領(lǐng)域知識(shí)的LDA主題模型”(項(xiàng)目編號(hào):YC2015-S304)
【分類(lèi)號(hào)】：G255.53
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本文編號(hào)：1938319