Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study

Dridi, Amna and Gaber, Mohamed Medhat and Azad, Raja Muhammad Atif and Bhogal, Jagdev (2022) Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study. Big Data and Cognitive Computing, 6 (1). p. 21. ISSN 2504-2289

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Abstract

The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science, especially with the rise of data science and the development of increasingly powerful computers. Among these approaches, some works have studied dynamism in scientific literature by employing text analysis techniques that rely on topic models to study the dynamics of research topics. Unlike topic models that do not delve deeper into the content of scientific publications, for the first time, this paper uses temporal word embeddings to automatically track the dynamics of scientific keywords over time. To this end, we propose Vec2Dynamics, a neural-based computational history approach that reports stability of k-nearest neighbors of scientific keywords over time; the stability indicates whether the keywords are taking new neighborhood due to evolution of scientific literature. To evaluate how Vec2Dynamics models such relationships in the domain of Machine Learning (ML), we constructed scientific corpora from the papers published in the Neural Information Processing Systems (NIPS; actually abbreviated NeurIPS) conference between 1987 and 2016. The descriptive analysis that we performed in this paper verify the efficacy of our proposed approach. In fact, we found a generally strong consistency between the obtained results and the Machine Learning timeline.

Item Type: Article
Identification Number: https://doi.org/10.3390/bdcc6010021
Dates:
DateEvent
15 February 2022Accepted
21 February 2022Published Online
Uncontrolled Keywords: scientific literature; computational linguistics; temporal word embedding; k-NN stability; machine learning
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Mohamed Gaber
Date Deposited: 21 Mar 2022 12:00
Last Modified: 21 Mar 2022 12:00
URI: http://www.open-access.bcu.ac.uk/id/eprint/12958

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