一种用于检测公共采购中指控性语言的级联无监督-监督NLP流水线

中文摘要 AI 辅助

公共采购涉及大量财政资源的分配，因此通过审计、控制和监测机制进行持续监督至关重要。然而，利益相关者的评论和公开的政府数据往往未得到充分利用，尽管它们有潜力揭示程序违规行为。为解决这一缺口，本文分析了厄瓜多尔官方公共采购系统（SOCE）的元数据，重点关注合同前阶段产生的参与者评论。我们提出一种混合建模框架，将无监督聚类与监督分类整合到自然语言处理（NLP）流水线中，以挖掘潜在模式并检测潜在的违规采购流程。使用Word2Vec、LLaMA和RoBERTa生成语义嵌入，随后采用高斯混合模型（GMMs）进行无监督聚类。接着应用监督分类阶段来识别指控性或举报人风格的评论。实验结果表明，结合领域训练的Word2Vec嵌入、基于GMM的聚类和随机森林分类器，即使在严重类别不平衡的情况下也能实现高准确率和召回率。这些发现表明，轻量级、领域适配的NLP架构可有效支持风险识别，在无需大规模计算基础设施的情况下提升公共采购系统的透明度。

英文摘要

Public procurement involves the allocation of substantial financial resources; therefore, continuous oversight through audits, controls, and monitoring mechanisms is essential. However, stakeholder comments and publicly available government data are often underutilized, despite their potential to reveal procedural irregularities. To address this gap, this paper analyzes metadata from Ecuador's Sistema Oficial de Contratación Pública (SOCE, Official Public Procurement System), with particular emphasis on participant comments generated during the pre-contractual phase. We propose a hybrid modeling framework that integrates unsupervised clustering and supervised classification within a natural language processing (NLP) pipeline to uncover latent patterns and detect potentially irregular procurement processes. Semantic embeddings are generated using Word2Vec, LLaMA, and RoBERTa, followed by Gaussian Mixture Models (GMMs) for unsupervised clustering. A supervised classification stage is then applied to identify accusatory or whistleblowing-style comments. Experimental results show that the combination of domain-trained Word2Vec embeddings, GMM-based clustering, and a Random Forest classifier achieves high precision and recall, even under severe class imbalance. These findings demonstrate that lightweight, domain-adapted NLP architectures can effectively support risk identification and enhance transparency in public procurement systems without requiring large-scale computational infrastructure.

发表机构

• Universidad San Francisco de Quito（基多圣弗朗西斯科大学）
• Universidad de Las Américas（美洲大学）
• Escuela Politécnica Nacional（国家理工学院）
• Carnegie Mellon University（卡内基梅隆大学）

机构由 AI 辅助整理，请以论文原文为准。