基于通用采购词汇表分类体系的零样本层次分类
Zero-Shot Hierarchical Classification on the Common Procurement Vocabulary Taxonomy
AI总结：
针对公共招标CPV分类体系中细粒度类别样本不足的问题，提出基于预训练语言模型的零样本层次分类方法，仅依赖标签描述即可有效预测低频及未见类别。
摘要：
对公共招标进行分类，对于受邀参与的公司以及审查欺诈活动而言都是一项有用的任务。为了方便参与者和公共行政部门执行该任务，欧盟提出了一套通用分类体系（通用采购词汇表，Common Procurement Vocabulary, CPV），这对于具有一定重要性的招标是强制性的；然而，在所有公共行政活动中，强制要求带有CPV标签的合同仅占少数。在真实的分类体系上进行分类会带来一些不容忽视的困难。首先，一些细粒度类别在训练集中的观测样本数量不足（如果有的话），而其他类别出现的频率则远高于平均水平（甚至高达数千倍）。为了克服这些困难，我们提出了一种零样本方法，该方法基于预训练语言模型，仅依赖标签描述并遵循标签分类体系。为了训练我们提出的模型，我们使用了工业数据，这些数据来自contrattipubblici.org，这是由SpazioDati s.r.l.提供的一项服务，收集了过去25年间意大利签订的公共合同。结果表明，与三种不同的基线相比，所提出的模型在分类低频类别时取得了更好的性能，并且还能够预测从未见过的类别。
英文摘要：
Classifying public tenders is a useful task for both companies that are invited to participate and for inspecting fraudulent activities. To facilitate the task for both participants and public administrations, the European Union presented a common taxonomy (Common Procurement Vocabulary, CPV) which is mandatory for tenders of certain importance; however, the contracts in which a CPV label is mandatory are the minority compared to all the Public Administrations activities. Classifying over a real-world taxonomy introduces some difficulties that can not be ignored. First of all, some fine-grained classes have an insufficient (if any) number of observations in the training set, while other classes are far more frequent (even thousands of times) than the average. To overcome those difficulties, we present a zero-shot approach, based on a pre-trained language model that relies only on label description and respects the label taxonomy. To train our proposed model, we used industrial data, which comes from contrattipubblici.org, a service by SpazioDati s.r.l. that collects public contracts stipulated in Italy in the last 25 years. Results show that the proposed model achieves better performance in classifying low-frequent classes compared to three different baselines, and is also able to predict never-seen classes.
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。
