Abstract
Both large and medium-sized companies are concerned with maintaining good procurement relationships with their preferred suppliers and engaging previously unknown ones. To this end, potentially interesting suppliers are periodically monitored and evaluated by interacting with them via e-procurement platforms. Such a task rapidly becomes time-consuming and error-prone since buyer-side users ask documents of different types to suppliers, before manually evaluating the compliance status of every single document. To overcome this problem, we integrated Information Extraction capabilities, based on supervised Named Entity Recognition (NER), in the EPICS e-procurement platform. The solution has been evaluated both quantitatively and qualitatively on real-world procurement documents. Results show that the proposed approach is able to achieve good information extraction accuracy concerning different procurement document categories written in the Italian language.
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Acknowledgements
The EPICS (E-Procurement Innovation For Challenging Scenarios) project has been co-funded by Programma del Regolamento regionale della Puglia per gli aiuti in esenzione n. 17 del 30/09/2014 (BURP n. 139 suppl. del 06/10/2014) titolo II capo 2 del regolamento generale aiuti ai programmi integrati promossi da medie imprese ai sensi dell’articolo 26 del Regolamento.
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Impedovo, A., Barracchia, E.P., Rizzo, G. (2022). Exploiting Named Entity Recognition for Information Extraction from Italian Procurement Documents: A Case Study. In: Pardede, E., Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Information Integration and Web Intelligence. iiWAS 2022. Lecture Notes in Computer Science, vol 13635. Springer, Cham. https://doi.org/10.1007/978-3-031-21047-1_5
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