Terminology Saturation: Detection, Measurement and Use

Victoria Kosa · Vadim Ermolayev

феб 2022. · Springer Nature

Е-књига

177

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О овој е-књизи

This book highlights an innovative approach for extracting terminological cores from subject domain-bounded collections of professional texts. The approach is based on exploiting the phenomenon of terminological saturation. The book presents the formal framework for the method of detecting and measuring terminological saturation as a successive approximation process. It further offers the suite of the algorithms that implement the method in the software and comprehensively evaluates all the aspects of the method and possible input configurations in the experiments on synthetic and real collections of texts in several subject domains. The book demonstrates the use of the developed method and software pipeline in industrial and academic use cases. It also outlines the potential benefits of the method for the adoption in industry.

О аутору

Victoria has recently defended her Ph.D. with the thesis entitled "A Method of Experimental Study of Terminological Saturation in Document Collections for Knowledge Elicitation" at the department of Computer Science of Zaporizhzhia National University (Ukraine). She is currently a self-employed researcher involved in an industrial consulting project with GroupBWT LLC. Her professional interests and competence are within the fields of Automated Terminology Recognition and Ontology Engineering.

Vadim is an associate professor at the Department of Computer Science of Zaporizhzhia National University (Ukraine). He is also the lead of Intelligent Systems Research Group. Throughout his career, he combines academic activities with different professional engagements in industry (as a research consultant) and public international organizations (as an expert) in knowledge and ontology engineering, semantic technologies, intelligent software systems, distributed artificial intelligence. A particular research topic that he focuses on in his research is capturing the dynamics and adaptability of real world in intelligent artefacts. His current research interests are within ontology engineering, ontology learning, and text mining.

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