Using Machine Learning Approaches to Identify Emergence: Case of Vehicle Related Patent Data

机译：使用机器学习方法识别紧急情况：车辆相关专利数据的案例

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Bibliometric studies have long used simple search strings, publications count, and word counts to track the emergence of technologies. Novel machine learning methods open new possibilities to study bibliometric data and use algorithmic approaches to uncover emergence of a technology. This study looks at the large and complex dataset of vehicle related patents to uncover emergence indicators. By using machine learning methods this study focuses on if, and to what extent different methods can produce patterns of emergence from the data directly. The data extracted from PATSTAT contains 711296 granted US patent abstracts between the years 1980 and 2014 resulting from a search for "vehicle" creating a complex dataset of technologies from automotive to medical applications. Using Latent Dirichlet Allocation and Dynamic Topic Modeling we show different emergence patterns. Finally, we discuss in detail the possibilities of using machine learning approaches to draw emergence dynamics of technologies.

机译：文献计量学研究长期以来一直使用简单的搜索字符串，出版物计数和字数统计来跟踪技术的出现。新颖的机器学习方法为研究文献计量数据和使用算法方法发现技术的出现开辟了新的可能性。这项研究着眼于与车辆相关的专利的庞大而复杂的数据集，以发现新兴指标。通过使用机器学习方法，本研究关注于不同的方法是否以及在何种程度上可以直接从数据中产生出现的模式。从PATSTAT提取的数据包含1980年至2014年间因研究“车辆”而创建的711296项美国专利摘要，这些“车辆”创建了从汽车到医疗应用的复杂技术数据集。使用潜在Dirichlet分配和动态主题建模，我们展示了不同的出现模式。最后，我们详细讨论了使用机器学习方法绘制技术涌现动态的可能性。

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《Portland International Conference on Management of Engineering and Technology》|2017年|1-8|共8页
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Samira Ranaei; Arho Suominen;
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Patents; Bibliometrics; Predictive models; Technological innovation; Databases; Data mining;

机译：专利;文献计量学;预测模型;技术创新;数据库;数据挖掘;

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Using Machine Learning Approaches to Identify Emergence: Case of Vehicle Related Patent Data

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