Text classification for private procurement: a survey and an analysis of future trends.
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| Title: | Text classification for private procurement: a survey and an analysis of future trends. |
|---|---|
| Authors: | Bellomi, Francesco1 (AUTHOR) francesco.bellomi@creactives.com, Cristani, Matteo2 (AUTHOR) matteo.cristani@univr.it |
| Source: | Information Technology & Management. Jun2026, Vol. 27 Issue 2, p159-171. 13p. |
| Subjects: | Electronic procurement, Language models, Machine translating, Computational linguistics, Corporate purchasing, Prompt engineering, Automatic classification |
| Abstract: | The development of techniques for text classification, categorization, clustering and segregation has a long history of applications in a variety of fields, including social network analysis, document archiving, business document processing. The field of private procurement, in which many application domains are included, is a small but very challenging area for the aforementioned concepts. After commerce globalization (in the nineties), e-commerce B2C explosion (in the years two-thousands) and the emergence of B2B international processes for e-procurement (in the years two-thousands-ten) we are now in a post-COVID era in which the internationalisation process has reached momentum. We are in a position of considering a front made up of multi-lingual, development differential and transparent market, for which comparison processes are ubiquitously required. In this survey we found major trends in the future of text classification employed in a multilingual, multicultural and non-standardized procurement processes. The usage of Large Language Models, and in particular the development of a specific field of post-processing of answers from LLM that is the dual component of prompt engineering, an emerging field in LLM, shall settle a new environment for procurement. We envision an application domain made of the dual usage of prompt engineering and post-processing algorithms to improve the performances of classification technologies for e-procurement. Moreover, the development of translation abilities of LLM as well as other approaches of machine translation will bring novel quality levels for these applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Information Technology & Management is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193682853 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Text classification for private procurement: a survey and an analysis of future trends. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bellomi%2C+Francesco%22">Bellomi, Francesco</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> francesco.bellomi@creactives.com</i><br /><searchLink fieldCode="AR" term="%22Cristani%2C+Matteo%22">Cristani, Matteo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> matteo.cristani@univr.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Information+Technology+%26+Management%22">Information Technology & Management</searchLink>. Jun2026, Vol. 27 Issue 2, p159-171. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electronic+procurement%22">Electronic procurement</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+translating%22">Machine translating</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+linguistics%22">Computational linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Corporate+purchasing%22">Corporate purchasing</searchLink><br /><searchLink fieldCode="DE" term="%22Prompt+engineering%22">Prompt engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+classification%22">Automatic classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The development of techniques for text classification, categorization, clustering and segregation has a long history of applications in a variety of fields, including social network analysis, document archiving, business document processing. The field of private procurement, in which many application domains are included, is a small but very challenging area for the aforementioned concepts. After commerce globalization (in the nineties), e-commerce B2C explosion (in the years two-thousands) and the emergence of B2B international processes for e-procurement (in the years two-thousands-ten) we are now in a post-COVID era in which the internationalisation process has reached momentum. We are in a position of considering a front made up of multi-lingual, development differential and transparent market, for which comparison processes are ubiquitously required. In this survey we found major trends in the future of text classification employed in a multilingual, multicultural and non-standardized procurement processes. The usage of Large Language Models, and in particular the development of a specific field of post-processing of answers from LLM that is the dual component of prompt engineering, an emerging field in LLM, shall settle a new environment for procurement. We envision an application domain made of the dual usage of prompt engineering and post-processing algorithms to improve the performances of classification technologies for e-procurement. Moreover, the development of translation abilities of LLM as well as other approaches of machine translation will bring novel quality levels for these applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Information Technology & Management is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10799-024-00444-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 159 Subjects: – SubjectFull: Electronic procurement Type: general – SubjectFull: Language models Type: general – SubjectFull: Machine translating Type: general – SubjectFull: Computational linguistics Type: general – SubjectFull: Corporate purchasing Type: general – SubjectFull: Prompt engineering Type: general – SubjectFull: Automatic classification Type: general Titles: – TitleFull: Text classification for private procurement: a survey and an analysis of future trends. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bellomi, Francesco – PersonEntity: Name: NameFull: Cristani, Matteo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1385951X Numbering: – Type: volume Value: 27 – Type: issue Value: 2 Titles: – TitleFull: Information Technology & Management Type: main |
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