基于BERT的植物健康公告分类模型的微调
Fine-tuning BERT-based models for Plant Health Bulletin Classification
摘要：
在数字化时代，农业的不同参与者产生了大量数据。这些数据包含了该领域中已经潜在的历史知识。这些知识使我们能够精确地研究全球或地方方面的自然灾害，然后改进风险预防任务并增加产量，这有助于应对人口增长和饮食习惯改变的挑战。特别是，《法国植物卫生公报》（BSV，因其在《法国植物卫生公报》中的名称而得名）提供了关于农业生产中植物检疫风险发展阶段的信息。然而，它们是用自然语言编写的，因此，机器和人类无法尽可能有效地利用它们。自然语言处理（NLP）技术旨在自动处理和分析大量自然语言数据。自2010年代以来，随着计算能力和并行化的增加，表示学习和深度学习方法在NLP中变得广泛。Transformers的双向编码表示（BERT）的最新进展启发我们重新思考植物健康管理领域的知识表示和自然语言理解。这项工作的目标是提出一种基于BERT的方法来自动分类BSV，使其数据易于索引。我们采样了200个BSV来微调预训练的BERT语言模型，并将其分类为害虫或/和疾病，我们展示了初步结果。
英文摘要：
In the era of digitization, different actors in agriculture produce numerous data. Such data contains already latent historical knowledge in the domain. This knowledge enables us to precisely study natural hazards within global or local aspects, and then improve the risk prevention tasks and augment the yield, which helps to tackle the challenge of growing population and changing alimentary habits. In particular, French Plants Health Bulletins (BSV, for its name in French Bulletin de Sant{é} du V{é}g{é}tal) give information about the development stages of phytosanitary risks in agricultural production. However, they are written in natural language, thus, machines and human cannot exploit them as efficiently as it could be. Natural language processing (NLP) technologies aim to automatically process and analyze large amounts of natural language data. Since the 2010s, with the increases in computational power and parallelization, representation learning and deep learning methods became widespread in NLP. Recent advancements Bidirectional Encoder Representations from Transformers (BERT) inspire us to rethink of knowledge representation and natural language understanding in plant health management domain. The goal in this work is to propose a BERT-based approach to automatically classify the BSV to make their data easily indexable. We sampled 200 BSV to finetune the pretrained BERT language models and classify them as pest or/and disease and we show preliminary results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
