🤖 AI Summary
This study addresses the lack of systematic approaches for constructing, storing, and sharing high-quality annotated corpora. It proposes a generalizable and reusable end-to-end methodology encompassing annotation guideline development, corpus annotation, data storage, sharing mechanisms, and value realization, with an emphasis on full lifecycle management and cross-domain applicability. Integrating linguistic annotation theory, data management standards, and collaborative research practices, the approach is articulated through a structured framework and illustrative examples to yield a clear and actionable guide. The resulting methodology provides standardized support for diverse research domains, significantly enhancing the efficiency and quality with which researchers can build and utilize annotated textual data.
📝 Abstract
This document, based on feedback from UMR TETIS members and the scientific literature, provides a generic methodology for creating annotation guidelines and annotated textual datasets (corpora). It covers methodological aspects, as well as storage, sharing, and valorization of the data. It includes definitions and examples to clearly illustrate each step of the process, thus providing a comprehensive framework to support the creation and use of corpora in various research contexts.