This paper summarizes the participation of the Laboratoire Informatique, Image et Interaction (L3i laboratory) of the University of La Rochelle in the Recognizing Ultra Fine-grained Entities (RUFES) track within the Text Analysis Conference (TAC) series of evaluation workshops. Our participation relies on two neural-based models, one based on a pre-trained and fine-tuned language model with a stack of Transformer layers for fine-grained entity extraction and one out-of-the-box model for within-document entity coreference. We observe that our approach has great potential in increasing the performance of fine-grained entity recognition. Thus, the future work envisioned is to enhance the ability of the models following additional experiments and a deeper analysis of the results.
翻译:本文件总结了罗歇尔大学La Rochelle实验室的Laboratoi Informatique、图像与互动(L3i实验室)在文本分析会议系列评价讲习班内参加确认超精锐实体(RUFES)轨道的情况,我们的参与依赖于两个基于神经的模型,一个基于预先培训、经过微调的语言模型,有一套用于精细提取实体的变异体层,另一个基于文件内实体共同参照的箱外模型。我们注意到,我们的方法在提高微精锐实体识别的绩效方面具有巨大潜力,因此,设想的未来工作是在进行更多的实验和对结果进行更深入的分析之后,提高模型的能力。