In:Theoretical Perspectives on Terminology: Explaining terms, concepts and specialized knowledge
Edited by Pamela Faber and Marie-Claude L'Homme
[Terminology and Lexicography Research and Practice 23] 2022
► pp. 219–236
Chapter 10Textual Terminology
Origins, principles and new challenges
Published online: 14 June 2022
https://doi.org/10.1075/tlrp.23.10con
https://doi.org/10.1075/tlrp.23.10con
Abstract
This chapter deals with Textual Terminology, an approach that emerged in the early 1990s along with other approaches presented in this book. One of its main characteristics is that it arose from an encounter between related disciplines that focused on the use of text mining (Natural Language Processing) and modeling tools (Knowledge Engineering and Artificial Intelligence). These disciplines have worked together to consider the new needs related to terminology, and their evolution. The tools developed and the diversification of needs have resulted in significant methodological developments in Textual Terminology. This chapter traces these developments and discusses the original bases, principles, challenges and perspectives that constitute Textual Terminology.
Article outline
- 1.Introduction
- 2.The origins of Textual Terminology
- 2.1New needs in relation to Terminology
- 2.2Corpus Linguistics and corpus-based Terminology
- 2.3Terminology, NLP and Knowledge Engineering
- 3.The principles of Textual Terminology today
- 3.1Steps in a textual terminology analysis
- 3.1.1Purpose of the analysis
- 3.1.2Compilation of the corpus
- 3.1.3Selection of tools
- 3.1.4Use of tools
- 3.1.5Validation of the results
- 3.1.6Integrating the results into linguistic paradigms
- 3.2Skills and protagonists
- 3.1Steps in a textual terminology analysis
- 4.Textual Terminology today: Challenges and perspectives
- 4.1New needs and perspectives
- 4.1.1New needs in Terminology
- 4.1.2Towards new kinds of need: Controlled natural languages
- 4.2Textual Terminology, NLP, and KE/AI: Towards a redefinition of the relationship
- 4.2.1From symbolic approaches to machine learning: Data quantity
- 4.2.2Analysing specialised corpora today: Methodology
- 4.1New needs and perspectives
- 5.Concluding remarks
Notes
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