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Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations
Citation key vashisth2019a
Author Vashisth, Gaurav and Mikhailov, Michael and Voigt-Antons, Jan-Niklas and Roller, Roland
Title of Book Proceedings of the BioNLP 2019 workshop
Pages 1–10
Year 2019
DOI 10.18653/v1/W19-5037
Location Florence, Italy
Address Stroudsburg, PA 18360, NJ, USA
Month aug
Note Online
Publisher The Association for Computational Linguistics
How Published Fullpaper
Abstract In research best practices can change over time as new discoveries are made and novel methods are implemented. Scientific publications reporting about the latest facts and current state-of-the-art can be possibly outdated after some years or even proved to be false. A publication usually sheds light only on the knowledge of the period it has been published. Thus, the aspect of time can play an essential role in the reliability of the presented information. In Natural Language Processing many methods focus on information extraction from text, such as detecting entities and their relationship to each other. Those methods mostly focus on the facts presented in the text itself and not on the aspects of knowledge which changes over time. This work instead examines the evolution in biomedical knowledge over time using scientific literature in terms of diachronic change. Mainly the usage of temporal and distributional concept representations are explored and evaluated by a proof-of-concept.
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