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3 results for “Open Information Extraction”
OIE4PA: Open Information Extraction for the Public Administration
<p>Tenders are powerful means of investment of public funds and represent a strategic development resource.<br> 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. <br> With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration.<br> Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. <br> 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.</p> <p>The dataset, compressed in a single zip file, is composed of:</p> <ul> <li>The corpus of 6,262 texts extracted from Italian public tenders (corpus_tenders)</li> <li>The training set of 1,600 annotated triples (training_set)</li> <li>The test set of 400 annotated triples (test_set)</li> <li>The set U of 14,096 triples used for the self-training (u_triples_dd)</li> <li>a compressed archive that contains both the extracted triples and the index for each supervised approach (extraction) <br> </li> </ul>
CompactIE: Compact Facts in Open Information Extraction [Dataset]
<p>Dataset for the paper <a href="https://arxiv.org/abs/2205.02880">"CompactIE: Compact Facts in Open Information Extraction"</a>. </p>
MOIED: Magi Open Information Extraction Dataset
<p><strong>Description</strong></p> <p>Magi Open Information Extraction Dataset (MOIED) is a Chinese Open IE dataset containing 7,618,181 records extracted from plain text across 3,319,763 webpages in various domains. Each record in the dataset consists of the (subject, predicate, object) tuple, the associated confidence score, and the context information. The dataset comprises 1,427,742 distinct facts of 272,522 entities and 117,731 predicates.</p> <p>A notable property of MOIED is that each distinct fact has multiple records with URLs referring to mentions in diverse contexts, which enables multiple-instance learning (MIL) and other correlative approaches.</p> <p>As a paragraph level Open IE dataset, at least 45.1% of the records in MOIED can only be extracted through synthesizing information from multiple sentences.</p> <p>Magi is an extraction engine that continuously learns from the Internet, which combines cross-referencing, timeline analysis, and other heuristics to mitigate the inevitable false positives in the extractions. All records in MOIED were randomly sampled from a database dump of <a href="https://magi.com/">magi.com</a> in January 2020. To provide more reliable evaluation results, human annotators examined the dataset and selected 19,161 verified records for the dev and test sets.</p> <p> </p> <p><strong>Disclaimers</strong></p> <ol> <li>The dataset is expected to be used in weakly supervised scenarios since the records in the training set are not human-annotated and could be imprecise or erroneous.</li> <li>Records are not guaranteed to be universally correct. The correctness of extractions should be evaluated based on contexts (specified by the URLs).</li> <li>The extraction was made at a certain time Magi visits the URL, thus it is not guaranteed that the URL is still accessible, or the content is unmodified since the extraction was conducted.</li> <li>Due to legal and regulatory issues, the webpage URLs are mostly ones accessible from Mainland China, yet, the content of certain webpages, as well as the extraction results, could be in violation of law and regulation of certain countries or regions in certain ways.</li> </ol>
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