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NLPropTest: Parsing English to Property-Based Tests with Categorial Grammars ...
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NLPropTest: Parsing English to Property-Based Tests with Categorial Grammars ...
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Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Pemanfaatan Bank-data Digital Dwibahasa dalam Kajian Terjemahan: Studi kasus padanan bahasa Indonesia untuk verba sinonim bahasa Inggris ROB & STEAL ...
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Pemanfaatan Bank-data Digital Dwibahasa dalam Kajian Terjemahan: Studi kasus padanan bahasa Indonesia untuk verba sinonim bahasa Inggris ROB & STEAL ...
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Chances and Challenges for Quantitative Approaches in Chinese Historical Phonology ...
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Computational Approaches to Historical Language Comparison ...
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The glyph project: The distinctiveness of written characters — online crowdsourcing for a typology of letter shapes ...
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Backchannel Behavior Influences the Perceived Personality of Human and Artificial Communication Partners
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Found speech and humans in the loop : Ways to gain insight into large quantities of speech
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Is a Wizard-of-Oz Required for Robot-Led Conversation Practice in a Second Language?
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Águas Lopes, José David; Cumbal, Ronald; Engwall, Olov. - : KTH, Tal-kommunikation, 2022. : KTH, Tal, musik och hörsel, TMH, 2022. : Springer Nature, 2022
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Using Machine Learning for Pharmacovigilance: A Systematic Review
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In: Pharmaceutics; Volume 14; Issue 2; Pages: 266 (2022)
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Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 21 (2022)
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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.
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Keyword:
computational linguistics; k -NN stability; machine learning; scientific literature; temporal word embedding
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URL: https://doi.org/10.3390/bdcc6010021
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