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In this paper, we present an efficient deep learning based approach toextract technology-related topics and keywords within scientific literature,and identify corresponding technologies within patent applications.Specifically, we utilize transformer based language models, tailored for usewith scientific text, to detect coherent topics over time and describe these byrelevant keywords that are automatically extracted from a large text corpus. Weidentify these keywords using Named Entity Recognition, distinguishing betweenthose describing methods, applications and other scientific terminology. Wecreate a large amount of search queries based on combinations of method- andapplication-keywords, which we use to conduct semantic search and identifyrelated patents. By doing so, we aim at contributing to the growing body ofresearch on text-based technology mapping and forecasting that leverages latestadvances in natural language processing and deep learning. We are able to maptechnologies identified in scientific literature to patent applications,thereby providing an empirical foundation for the study of science-technologylinkages. We illustrate the workflow as well as results obtained by mappingpublications within the field of neuroscience to related patent applications.

Mapping Complex Technologies via Science-Technology Linkages; The Case of Neuroscience -- A transformer based keyword extraction approach