Publikation

Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations

Gaurav Vashisth, Jan-Niklas Voigt-Antons, Michael Mikhailov, Roland Roller

In: Proceedings of the 18th BioNLP Workshop and Shared Task. Workshop on Current Trends in Biomedical Natural Language Processing (BioNLP-2019) Florence, Italy Seiten 348-358 Association for Computational Linguistics 2019.

Abstrakt

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 Scientific publications reporting about the latest facts and current state-of-the-art can be possibly outdated after some years or even proved to be false.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.

Weitere Links

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence