D'eveloppement automatique de lexiques pour les concepts 'emergents : une exploration m'ethodologique
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Abstract (arXiv)
This paper presents the development of a lexicon centered on emerging concepts, focusing on non-technological innovation. It introduces a four-step methodology that combines human expertise, statistical analysis, and machine learning techniques to establish a model that can be generalized across multiple domains. This process includes the creation of a thematic corpus, the development of a Gold Standard Lexicon, annotation and preparation of a training corpus, and finally, the implementation of learning models to identify new terms. The results demonstrate the robustness and relevance of our approach, highlighting its adaptability to various contexts and its contribution to lexical research. The developed methodology promises applicability in conceptual fields.
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References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
- 2023LLMaAA: Making Large Language Models as Active Annotatorscited by 79
- 2018A Short Survey on Sense-Annotated Corporacited by 2
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