D\'eveloppement automatique de lexiques pour les concepts \'emergents : une exploration m\'ethodologique
<|reference_start|>D\'eveloppement automatique de lexiques pour les concepts \'emergents : une exploration m\'ethodologique: 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.<|reference_end|>
@article{kyriakoglou2024d\'eveloppement,
    title={D\'eveloppement automatique de lexiques pour les concepts \'emergents :
  une exploration m\'ethodologique},
    author={Revekka Kyriakoglou, Anna Pappa, Jilin He, Antoine Schoen, Patricia
  Laurens, Markarit Vartampetian, Philippe Laredo, Tita Kyriacopoulou},
    journal={arXiv preprint arXiv:2406.10253},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10253},
    primaryClass={cs.CL cs.IR cs.LG}
}