Automatic Generation of Common Procurement Vocabulary Codes
Authors: Lucia Siciliani, Emanuele Tanzi, Pierpaolo Basile, Pasquale Lops (Università di Bari Aldo Moro, 2023)
Topic: In-domain IR and IE

Abstract
This study highlights the critical role of tenders as tools for public fund investment and strategic development. Enhancing e-procurement solutions can significantly aid in managing and investing these funds effectively. In e-procurement, each tender is assigned a code through the Common Procurement Vocabulary (CPV) to classify its subject matter. Our research addresses the challenge of automating CPV code assignment for tenders, approaching it both as a classification problem and a generative task. For model development and evaluation, we constructed a dataset of 5 million Italian tenders sourced from the National Anti-Corruption Authority (Autorità nazionale anticorruzione - ANAC) website. Results indicate that text classification methods currently offer the best performance for this task. However, the findings also demonstrate the potential of generative models to address limitations in existing classification techniques, offering promising avenues for refining CPV code assignment and, in turn, optimizing procurement processes and enhancing efficiency in public sector operations.

Introduction
The role of tenders as means of investment of public funds and as vehicles of strategic development is nowadays crucial. For this reason, developing and enabling new solutions for e-procurement procedures can help to manage and invest funds. In e-procurement, the Common Procurement Vocabulary (CPV) allows assigning a code that classifies its subject to each tender. This study addresses the challenge of automatically assigning a CPV code to a tender. We tackle this problem in two different ways: as a classification problem and as a generative task. To develop and test our models, we build a dataset of 5M Italian tenders extracting them from the National Anti-Corruption Authority (Autorità nazionale anticorruzione - ANAC) website. Results show that text classifier approaches exhibit superior performance in this regard. However, they also reveal the potential of generative models in overcoming the limitations of existing classification methods for CPV code assignment in tender classification, providing valuable insights for improving procurement processes and enhancing efficiency in public sector operations. Knowledge organization systems (KOS), such as thesauri, gazetteers, lexical databases, ontologies, and classification systems, are used by institutions to organize large data collections, e.g. documents, web pages, and texts. Using a standard format guarantees semantic interoperability and allows for a faster exchange of information. Public procurement represents a field where adopting such systems can bring many advantages. On one hand, citizens can access data more easily, enabling more straightforward communication with institutions, which can help streamline many bureaucratic processes. Concurrently, adopting KOS systems in procurement has profound implications for professionals working within public administrations. In fact, these systems can serve as invaluable tools, offering support in the day-to-day activities of public sector employees. Integrating advanced technologies will facilitate a paradigm shift towards higher productivity and efficiency. Tasks that were once labor-intensive and time-consuming can now be executed with greater precision and speed, allowing public administrators to focus on more strategic and value-driven aspects of their roles. For this reason, in the field of public procurement, the European Union developed a Common Procurement Vocabulary (CPV) that identifies the subject of a tender. The adoption of the CPV also allows companies to find new public contracts easily, thus fostering competitiveness. The CPV is structured as a tree of codes comprising 9 digits, eight plus a check digit, and specifies whether the tender in question refers to supplies, works or services covered by the contract. Each digit indicates progressively finer-grained classifications. More specifically, each CPV is composed as follows: - the first two digits identify the divisions (e.g. 71000000-8 Servizi architettonici, di costruzione, ingegneria e ispezione (Architectural, construction, engineering and inspection services)); - the first three digits identify the groups (e.g. 71300000-1 Servizi di ingegneria (Engineering services)); - the first four digits identify the classes (e.g. 71310000-4 Servizi di consulenza ingegneristica e di costruzione (Consultative engineering and construction services)); - the first five digits identify the categories (e.g. 71311000-1 Servizi di consulenza in ingegneria civile (Civil engineering consultancy services)); - each of the last three digits provides an additional degree of precision within each category (e.g. 71311210-6 Servizi di consulenza stradale (Highways consultancy services)); - a ninth digit serves to verify the previous digits. Examples of CPV codes are: 30200000-1 (Computer equipment and supplies), 30230000-0 (Computer hardware), and S30231000-7 (Computers and printers). The supplementary vocabulary can be used to complete the description of the subject of a contract. The items consist of an alphanumeric code corresponding to a denomination that allows you to provide further details on the specific nature or destination of the asset to be purchased. The alphanumeric code is structured as follows: - a first level, consisting of a letter corresponding to a section (e.g. A Materiali (Materials)); - a second level, consisting of a letter corresponding to a group (e.g. AA Metalli e leghe (Metal and alloy)); - a third level, consisting of two digits corresponding to the attribute (AA02-4 Alluminio (Aluminium)); - the last digit is used to verify the previous ones. Examples of supplementary codes are the following: AA01-1 Metal, or UB05-6 Office items. The main vocabulary comprises 9,454 terms and, more specifically, 45 divisions, 272 groups, 1,002 classes, 2,379 categories, and 5,756 sub-categories. Assigning a CPV to a tender is a task which is accomplished by RUPs (Responsabile Unico del Procedimento, i.e. Tender’s Managers), however, given the high number of terms, it is really difficult even for human experts to identify the right CPV to use. For this reason, despite assuring a fine-grained classification, the high number of labels frequently leads to errors in the CPV assignment like typos or wrong interpretation of the description of each code. Another phenomenon is represented by the skewed usage of the codes as there is a small number of CPVs which are more known and thus used more frequently while a large number of CPVs are underused. Given these premises, in this work, we propose a method for automatically classifying tenders to their CPV codes. The paper is organized as follows: Section 2 provides an overview of the approaches available at the state of the art, Section 3 contains the details of the proposed solution, Section 4 reports the results obtained by the evaluation of our model, and finally Section 5 closes the paper.

Conclusion
In this paper, we tackle the challenge of categorizing a tender by aligning it with the comprehensive Common Procurement Vocabulary (CPV), i.e. a meticulously curated European lexicon of codes designed to precisely identify the subject matter of a tender. The complexity of this task lies in the diverse nature of procurement scenarios, where each tender has its own description and requirements. The CPV emerges as a fundamental tool in deciphering the procurement language, trying to define a European dictionary allowing interoperability among different countries. Our proposed methodologies encompass two distinctive approaches: the former relies on a conventional text classification paradigm, whereas the latter leverages a generative strategy hinging on the encoder-decoder architecture as conceptualized by the T5 model. In our systematic exploration of the system’s proficiency in discerning the accurate division of a tender, specifically on the initial two digits of the CPV, it becomes evident that text classifier approaches provide the best results. Nevertheless, a noteworthy result surfaces when we focus on the holistic identification of the entire CPV through a descriptive context. In this context, the generative approaches exhibit commendable efficacy, demonstrating promising outcomes. Notably, these generative techniques surpass established baselines constructed through conventional keyword-centric search engines, attesting to their heightened capabilities in nuanced comprehension and contextual inference.