Tenders or technical terms contain a large quantity of both technical, legal, managerial information mixed in a nested and complex net of relationships. Extracting technical and design information from a document whose aim is both legal and technical, and that is written using several specific jargons, is not a trivial task: the purpose of the research is to try to detect, extract, split and assign information from the text of a tender in an automatic way. It means being able to understand technical and legal terms and organize them in multiple ways: according to product structure, internal organisational structure, etc. The focus is in providing a handy tool that could speed up and facilitate human analysis and allow tackling also the process of transforming customer's requirements into design specifications. The approach chosen to overcome the various issues is to support state-of-the-art Computational Linguistic tools with a wide Knowledge Base. The latter has been constructed both manually and automatically and comprises not only keywords but also concepts, relationships and regular expressions. The implementation of the methodology has been carried out during a project for AnsaldoBreda S.p.A. (now Hitachi Rail Europe). A case study about the tender for a high-speed train has been included to show the functioning and output of the entire software system. (C) 2020 Elsevier B.V. All rights reserved.

Text mining tool for translating terms of contract into technical specifications: Development and application in the railway sector

Dell'Orletta F;
2021

Abstract

Tenders or technical terms contain a large quantity of both technical, legal, managerial information mixed in a nested and complex net of relationships. Extracting technical and design information from a document whose aim is both legal and technical, and that is written using several specific jargons, is not a trivial task: the purpose of the research is to try to detect, extract, split and assign information from the text of a tender in an automatic way. It means being able to understand technical and legal terms and organize them in multiple ways: according to product structure, internal organisational structure, etc. The focus is in providing a handy tool that could speed up and facilitate human analysis and allow tackling also the process of transforming customer's requirements into design specifications. The approach chosen to overcome the various issues is to support state-of-the-art Computational Linguistic tools with a wide Knowledge Base. The latter has been constructed both manually and automatically and comprises not only keywords but also concepts, relationships and regular expressions. The implementation of the methodology has been carried out during a project for AnsaldoBreda S.p.A. (now Hitachi Rail Europe). A case study about the tender for a high-speed train has been included to show the functioning and output of the entire software system. (C) 2020 Elsevier B.V. All rights reserved.
Campo DC Valore Lingua
dc.authority.ancejournal COMPUTERS IN INDUSTRY en
dc.authority.orgunit Istituto di linguistica computazionale "Antonio Zampolli" - ILC en
dc.authority.people Fantoni G en
dc.authority.people Coli E en
dc.authority.people Chiarello F en
dc.authority.people Apreda R en
dc.authority.people Dell'Orletta F en
dc.authority.people Pratelli G en
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dc.date.accessioned 2024/02/21 05:22:27 -
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dc.date.firstsubmission 2024/12/20 13:10:59 *
dc.date.issued 2021 -
dc.date.submission 2025/01/24 15:16:33 *
dc.description.abstracteng Tenders or technical terms contain a large quantity of both technical, legal, managerial information mixed in a nested and complex net of relationships. Extracting technical and design information from a document whose aim is both legal and technical, and that is written using several specific jargons, is not a trivial task: the purpose of the research is to try to detect, extract, split and assign information from the text of a tender in an automatic way. It means being able to understand technical and legal terms and organize them in multiple ways: according to product structure, internal organisational structure, etc. The focus is in providing a handy tool that could speed up and facilitate human analysis and allow tackling also the process of transforming customer's requirements into design specifications. The approach chosen to overcome the various issues is to support state-of-the-art Computational Linguistic tools with a wide Knowledge Base. The latter has been constructed both manually and automatically and comprises not only keywords but also concepts, relationships and regular expressions. The implementation of the methodology has been carried out during a project for AnsaldoBreda S.p.A. (now Hitachi Rail Europe). A case study about the tender for a high-speed train has been included to show the functioning and output of the entire software system. (C) 2020 Elsevier B.V. All rights reserved. -
dc.description.affiliations Univesrità di Pisa; Università di Pisa; Erre Quadro Srl; ILC-CNR; Hitachi Rail SpA -
dc.description.allpeople Fantoni, G; Coli, E; Chiarello, F; Apreda, R; Dell'Orletta, F; Pratelli, G -
dc.description.allpeopleoriginal Fantoni G., Coli E., Chiarello F., Apreda R., Dell'Orletta F., Pratelli G. en
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dc.subject.keywordseng Contract terms -
dc.subject.keywordseng Technical requirements -
dc.subject.keywordseng Natural language processing -
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dc.title Text mining tool for translating terms of contract into technical specifications: Development and application in the railway sector en
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