Relation Extraction (RE) is a fundamental task in natural language processing, aimed at deducing semantic relationships between entities in a text. Traditional supervised extraction methods relation extraction methods involve training models to annotate tokens representing entity mentions, followed by predicting the relationship between these entities. However, recent advancements have transformed this task into a sequence-to-sequence problem. This involves converting relationships between entities into target string, which are then generated from the input text. Thus, language models now appear as a solution to this task and have already been used in numerous studies, with various levels of refinement, across different domains. The objective of the present study is to evaluate the contribution of large language models (LLM) to the task of relation extraction in a specific domain (in this case, the economic domain), compared to smaller language models. To do this, we considered as a baseline a model based on the BERT architecture, trained in this domain, and four LLM, namely FinGPT specific to the financial domain, XLNet, ChatGLM, and Llama3, which are generalists. All these models were evaluated on the same extraction task, with zero-shot for the general-purpose LLM, as well as refinements through few-shot learning and fine-tuning. The experiments showedthat the best performance in terms of F-score was achieved with fine-tuned LLM, with Llama3 achieving the highest performance.

La projection de patrons lexico-syntaxiques sur corpus est une des manières privilégiées pour identifier des relations sémantiques précises entre éléments lexicaux. Dans cet article, nous proposons d’étendre la notion de patron en prenant en compte la sémantique que véhiculent les éléments de structure d’un document (définitions, titres, énumérations) dans l’identification de relations. Nous avons testé cette hypothèse dans le cadre de la construction d’ontologies à partir de textes fortement structurés du domaine de la cartographie.