Machine learning and natural language processing on the patent corpus: Data, tools, and new measures

被引：48

作者：

Balsmeieri, Benjamin ^{[1
]}

Assaf, Mohamad ^{[2
,3
]}

Chesebro, Tyler ^{[4
]}

Fierro, Gabe ^{[4
]}

Johnson, Kevin ^{[4
]}

Johnson, Scott ^{[4
]}

Li, Guan-Cheng ^{[2
]}

Lueck, Sonja ^{[5
]}

O'Reagan, Doug ^{[2
]}

Yeh, Bill ^{[4
]}

Zang, Guangzheng ^{[4
]}

Fleming, Lee ^{[2
]}

机构：

[1] Univ Luxembourg, Ctr Res Econ & Management, Esch Sur Alzette, Luxembourg

[2] Univ Calif Berkeley, Coleman Fung Inst Engn Leadership, Berkeley, CA 94720 USA

[3] Amer Univ Beirut, Dept Elect & Comp Engn, Beirut, Lebanon

[4] Univ Calif Berkeley, Elect Engn & Comp Sci, Berkeley, CA USA

[5] Univ Paderborn, Dept Econ, Paderborn, Germany

来源：

JOURNAL OF ECONOMICS & MANAGEMENT STRATEGY | 2018年 / 27卷 / 03期

基金：

美国国家科学基金会;

关键词：

database; disambiguation; machine learning; natural language processing; patent; social networks; NETWORKS;

D O I：

10.1111/jems.12259

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Drawing upon recent advances in machine learning and natural language processing, we introduce new tools that automatically ingest, parse, disambiguate, and build an updated database using U.S. patent data. The tools identify unique inventor, assignee, and location entities mentioned on each granted U.S. patent from 1976 to 2016. We describe data flow, algorithms, user interfaces, descriptive statistics, and a novelty measure based on the first appearance of a word in the patent corpus. We illustrate an automated coinventor network mapping tool and visualize trends in patenting over the last 40 years. Data and documentation can be found at https://console.cloud.google.com/launcher/partners/patents-public-data.

引用

页码：535 / 553

页数：19

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