水电许可文件中环境缓解措施的检索增强分类

中文摘要 AI 辅助

识别和分类联邦能源监管委员会水电许可文件中的环境缓解义务是一项需要深厚领域专业知识的劳动密集型任务。我们将此问题表述为针对结构化135类分类体系的多标签分类问题，并解决标签严重稀缺的核心挑战：135个类别中有40个无训练样本，26个少于5个。基于监督式BERT（Bidirectional Encoder Representations from Transformers）的流水线在代表性良好的类别上虽有效，但无论采用何种增强策略，在未见类别上的F1值均为0。我们引入检索增强生成（RAG）流水线，该流水线基于检索到的类别定义进行分类，实现了全标签空间的零样本泛化。我们进一步提出混合系统，将BERT检测与RAG分类相结合，利用微调检测的高召回率和检索增强推理的零样本覆盖范围。在2017年全部许可文件（5860个段落、135个类别）上评估，混合系统的微平均F1值为0.524，在所有训练支持区间均优于仅BERT流水线（0.477）和仅RAG流水线（0.416）。

英文摘要

Identifying and classifying environmental mitigation obligations in Federal Energy Regulatory Commission hydropower licensing documents is a labor-intensive task requiring deep domain expertise. We formulate this as a multi-label classification problem over a structured 135-category taxonomy and address the central challenge of severe label scarcity: 40 of 135 categories have no training examples, and 26 have fewer than five. A supervised Bidirectional Encoder Representations from Transformers (BERT)-based pipeline, while effective on well-represented categories, achieves F1 of zero on unseen classes regardless of augmentation strategy. We introduce a Retrieval-Augmented Generation (RAG) pipeline that conditions classification on retrieved category definitions, enabling zero-shot generalization across the full label space. We further propose a hybrid system that combines BERT detection with RAG classification, exploiting the high recall of fine-tuned detection and the zero-shot coverage of retrieval-augmented reasoning. Evaluated on the full set of 2017 license documents (5,860 paragraphs, 135 categories), the hybrid achieves a Micro F1 of 0.524, outperforming the BERT-only pipeline (0.477) and the RAG-only pipeline (0.416) across all training-support buckets.

发表机构

• Oak Ridge National Laboratory（橡树岭国家实验室）
• Farragut High School（法拉格特高中）

机构由 AI 辅助整理，请以论文原文为准。