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A topic models based framework for detecting and forecasting emerging technologies

机译：基于主题模型检测和预测新兴技术的框架

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摘要

The identification of emerging technologies can bring valuable intelligence to enterprises and countries determining research and development (R&D) priorities. Emerging technologies are closely related to emerging topics in terms of several well-documented attributes: relatively fast growth, radical novelty and prominent impact. Our previous work on detecting and forecasting emerging topics is adapted to measure technology emergence, but the dynamic influence model (DIM) is replaced by the topical n-grams (TNG) model in this framework to nominate several emerging technologies in technical terms and to exploit the potential of topic models. Hence, technologies are viewed as term-based themes in this study. Three indicators are designed to reflect the above attributes: the fast growth indicator, the radical novelty indicator and the prominent impact indicator. The relatively fast growth indicator is calculated from the results of the TNG model and the radical novelty indicator comes from the citation influence model (CIM). As for the prominent impact indicator, the involving authors are used after name disambiguation and credit allocation. The following fields are utilized to develop the models: title, abstract, keywords-author, publication year, byline information, and cited references. We participated in the 2018-2019 Measuring Tech Emergence Contest with the proposed method, and 8 out of 10 submitted ones met the contest organizer's criteria of technology emergence. Criteria included the percentage of high growth terms out of total terms provided, the degree of growth of the terms, and the frequency of those high growth terms across the dataset. Then, a qualitative assessment of overall methodology was conducted by three judges. In the end, we won Second Prize in the contest.
机译：识别新兴技术可以为企业和国家确定研究和发展（研发）优先事项带来宝贵的情报。新兴技术与几种记录良好的属性方面的新兴主题密切相关：相对较快的增长，激进的新颖性和突出的影响。我们以前发现和预测的新课题工作，适用于测量技术的出现，但动态的影响力模型（DIM）是由局部的n-gram取代了这种架构在技术方面并利用提名几个新兴技术（TNG）模型主题模型的潜力。因此，技术在本研究中被视为基于术语的主题。三个指标旨在反映上述属性：快速增长指标，激进的新奇指标和突出的影响指标。相对较快的生长指标是根据TNG模型的结果计算的，并且激进的新奇指标来自引文影响模型（CIM）。至于突出的影响指标，涉及的作者在命名歧义和信用分配之后使用。使用以下字段来开发模型：标题，摘要，关键字 - 作者，发布年份，按键信息和引用的引用。我们参加了2018-2019次测量技术出现竞赛，拟议的方法，10个提交的8个符合竞赛组织者的技术出现标准。标准包括总条款的高增长条款百分比，条款的增长程度以及数据集中的那些高增长术语的频率。然后，三名法官进行了对整体方法的定性评估。最后，我们在比赛中获得二等奖。

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  • 来源
    《Technological forecasting and social change》 |2021年第1期|120366.1-120366.14|共14页
  • 作者

    Xu Shuo; Hao Liyuan; Yang Guancan; Lu Kun; An Xin;

  • 作者单位

    Beijing Univ Technol Coll Econ & Management Res Base Beijing Modern Mfg Dev 100 PingLeYuan Beijing 100124 Peoples R China;

    Beijing Univ Technol Coll Econ & Management Res Base Beijing Modern Mfg Dev 100 PingLeYuan Beijing 100124 Peoples R China;

    Renmin Univ China Sch Informat Resource Management 59 Zhongguancun St Beijing 100872 Peoples R China;

    Univ Oklahoma Sch Lib & Informat Studies 401 W Brooks St Norman OK 73072 USA;

    Beijing Forestry Univ Sch Econ & Management 35 Qinghua East Rd Beijing 100083 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Emerging Technology; Topic Model; Topical N-Grams Model; Name Disambiguation; Credit Allocation;

    机译：新兴技术;主题模型;主题n-grams模型;名称消歧;信用分配;

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