Zero-Shot Hierarchical Classification on the Common Procurement Vocabulary Taxonomy

TL;DR

This paper introduces a zero-shot hierarchical classification method using pre-trained language models to classify public procurement tenders according to the EU's CPV taxonomy, especially effective for rare and unseen classes.

Contribution

The paper proposes a novel zero-shot hierarchical classification approach leveraging label descriptions and taxonomy structure, trained on Italian public contract data.

Findings

Outperforms baselines on low-frequency classes

Capable of predicting unseen classes

Effective in real-world procurement classification

Abstract

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.