用关键词和对比学习实现法律文本高精度快速分类
ReLeVAnT: Relevance Lexical Vectors for Accurate Legal Text Classification
- 基于词袋与对比评分匹配，提取文档间判别性特征
- 在LexGLUE数据集上达99.3%准确率与98.7%F1值
- 仅需一次关键词提取，适合资源受限场景
从非结构化数据中对法律文书进行分类在下游任务中具有重要意义，例如起草诉状、备忘录、大纲，以及案卷摘要、检索系统构建和训练数据整理。现有方法依赖结构化数据、元数据或大模型提取的元数据，且需要大量计算资源。本文提出ReLeVAnT框架，专注于利用不同类别文档间的判别性特征进行二分类。该框架结合n-gram处理、对比分数匹配与浅层神经网络，通过一次性对整个语料库提取关键词，再由浅层分类器实现高效分类，在LexGLUE数据集上达到99.3%准确率与98.7% F1得分。
原文摘要 · Abstract (English)
The classification of legal documents from an unstructured data corpus has several crucial applications in downstream tasks. Documents relevant to court filings are key in use cases such as drafting motions, memos, and outlines, as well as in tasks like docket summarisation, retrieval systems, and training data curation. Current methods classify based on provided metadata, LLM-extracted metadata, or multimodal methods. These methods depend on structured data, metadata, and extensive computational power. This task is approached from a perspective of leveraging discriminative features in the documents between classes. The authors propose ReLeVAnT, a framework for legal document binary classification. ReLeVAnT utilises n-gram processing, contrastive score matching, and a shallow neural network as the primary drivers for discriminative classification. It leverages one-time keyword extraction per corpus, followed by a shallow classifier to swiftly and reliably classify documents with 99.3% accuracy and 98.7% F1 score on the LexGLUE dataset.
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