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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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Arc-Eager Construction Provides Learning Advantage Beyond Stack Management
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Language and Reasoning by Entropy Fractals
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In: Signals ; Volume 2 ; Issue 4 ; Pages 44-770 (2021)
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Learning meaning representations for text generation with deep generative models ...
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Cao, Kris. - : Apollo - University of Cambridge Repository, 2020
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Modelling speaker adaptation in second language learner dialogue ...
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Towards Programming in Natural Language: Learning New Functions from Spoken Utterances ...
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Модели и методы анализа тональности в текстах на башкирском языке ... : Models and methods for sentiment analysis of texts in Bashkir language ...
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Pathways to the Native Storyteller: a method to enable computational story understanding
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In: College of Computing and Digital Media Dissertations (2020)
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Towards Programming in Natural Language: Learning New Functions from Spoken Utterances
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In: International journal of semantic computing, 14 (2), 249–272 ; ISSN: 1793-351X, 1793-7108 (2020)
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Modelling speaker adaptation in second language learner dialogue
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Learning meaning representations for text generation with deep generative models
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Cao, Kris. - : University of Cambridge, 2019. : Department of Computer Science and Technology, 2019. : Clare, 2019
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Learnability and Overgeneration in Computational Syntax
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Describing and Classifying Post-Mortem Content on Social Media
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 12 No. 1 (2018): Twelfth International AAAI Conference on Web and Social Media ; 2334-0770 ; 2162-3449 (2018)
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hr500k – A Reference Training Corpus of Croatian.
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In: Conference papers (2018)
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Comparative analysis of algorithmic approaches for auto-coding with ICD-10-AM and ACHI
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Modeling common sense knowledge via scripts ... : Modellierung von Weltwissen mit Skripten ...
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Modeling common sense knowledge via scripts ; Modellierung von Weltwissen mit Skripten
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