基于层级多标签的农业病虫害问句分类方法

随着信息化技术的快速发展，农户通过线上智能农业问答系统解决线下农业病虫害问题已成为趋势。问句分类在问答系统中发挥着至关重要的作用，其准确性直接决定了最终返回答案的正确性。传统的单标签文本分类模型难以直接准确捕捉到农业病虫害问句的确切意图，而且由于缺乏大规模公开的农业病虫害问句语料，使得现有研究具有一定的难度。为此，本文基于树状结构构建了一个农业病虫害问句层级分类体系，由问句模糊性向精确性逐层细化分类，旨在克服农业问句的语义复杂性；此外，引入对抗训练方法，通过构建对抗样本并将其与原始样本一同用于大规模语言模型的训练，以提高模型泛化能力，同时缓解了因语料不足而产生的问题。通过对真实问答语料库的实验验证，本文提出的方法能够提升农业病虫害问句的分类性能，可为农业病虫害自动问答系统提供有效的问句意图识别。

Hierarchical Multi-label Classification of Agricultural Pest and Disease Interrogative Questions

With the rapid advancement of information technology, it has become a trend for farmers to address offline agricultural issues through online intelligent question-and-answer systems. Question classification plays a crucial role in question-and-answer systems, as its accuracy directly determines the correctness of the final answers. Traditional single-label text classification models often struggle to accurately capture the precise intent of agricultural queries. Moreover, the lack of large-scale publicly available query datasets about agricultural pest and disease poses a significant challenge to existing research methods. To address these challenges, a hierarchical classification framework for queries about agricultural pest and disease was established based on a tree-like structure. This framework progressively refined the classification from the ambiguity of queries towards precision, aiming to overcome the semantic complexity of agricultural queries. Additionally, adversarial training method was introduced. By constructing adversarial samples and incorporating them into the training of large-scale language models, the model''s generalization capabilities were enhanced, while mitigating issues arising from limited training data. Experimental validation conducted on real question-and-answer corpora demonstrated that the proposed method significantly enhanced the classification performance of queries about agricultural pest and disease. The research result can provide an effective means of identifying the intent behind agricultural queries, thereby offering support for advancing agricultural informatization.