OIE4PA: Open Information Extraction for the Public Administration

Tenders are powerful means of investment of public funds and represent a strategic development resource. Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration. Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. For evaluation purposes, we built a dataset composed of 2,000 triples extracted from Italian tenders, which have been manually annotated by two human experts. The dataset, compressed in a single zip file, is composed of: The corpus of 6,262 texts extracted from Italian public tenders (corpus_tenders) The training set of 1,600 annotated triples (training_set) The test set of 400 annotated triples (test_set) The set U of 14,096 triples used for the self-training (u_triples_dd) a compressed archive that contains both the extracted triples and the index for each supervised approach (extraction)We acknowledge the support of the PNRR project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI (CUP H97G22000210007) under the NRRP MUR program funded by the NextGenerationEU