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作者:

Xu, Shuo (Xu, Shuo.) (学者:徐硕) | Li, Ling (Li, Ling.) | An, Xin (An, Xin.) | Hao, Liyuan (Hao, Liyuan.) | Yang, Guancan (Yang, Guancan.)

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摘要:

Scientific publications and patents are usually viewed as respective proxies of scientific research and technical development. There is considerable effort spent towards establishing topic linkages between science and technology with the lexical- or topic-based approaches. However, due to the heterogeneity between scholarly articles and patents in terms of purpose, statement, and quality, the performance is not satisfactory. To understand the difficulties of topic linkages and improve the performance, a framework is proposed to detect the commonality and specialty between scientific publications and patents from the two perspectives: linguistic characteristics and thematic structures. Extensive experimental results on the DrugBank dataset discover five commonness and five significant differences in terms of linguistic characteristics. For example, nouns are used most frequently among them, and scientific publications contain more word tokens than patent documents, but patents have usually longer sentences and use more clauses. In the meanwhile, common and special thematic structures are also uncovered between scientific publications and patents. The themes about general description in the pharmaceutical field are shared by two heterogeneous resources. The scientific publications tend to explain the disease mechanism and the medication content, while patents bias towards the preparation and practical application of drugs.

关键词:

Stopword identification Linguistic characteristics Patent Scientific publication Multi-collection topic model

作者机构:

  • [ 1 ] [Xu, Shuo]Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China
  • [ 2 ] [Li, Ling]Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China
  • [ 3 ] [Hao, Liyuan]Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China
  • [ 4 ] [An, Xin]Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China
  • [ 5 ] [Yang, Guancan]Renmin Univ China, Sch Informat Resource Management, Beijing 100872, Peoples R China

通讯作者信息:

  • [An, Xin]Beijing Forestry Univ, Sch Econ & Management, Beijing 100083, Peoples R China

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来源 :

SCIENTOMETRICS

ISSN: 0138-9130

年份: 2021

期: 9

卷: 126

页码: 7445-7475

3 . 9 0 0

JCR@2022

ESI学科: SOCIAL SCIENCES, GENERAL;

ESI高被引阀值:53

JCR分区:2

被引次数:

WoS核心集被引频次: 11

SCOPUS被引频次: 16

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 3

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