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8 results for “Historical German”
Corpus of Historical German, Bochum
<p>This publication contains the datasets and annotation scheme from the paper: Stefanie Dipper, Cora Haiber, Anna Maria Schröter, Alexandra Wiemann, Maike Brinkschulte (2024) Universal Dependencies: Extensions for Modern and Historical German. In: Proceedings of LREC-COLING 2024.</p>
GT4HistOCR: Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin
<p><strong>GT4HistOCR</strong> contains ground truth for research in Optical Character Recognition (OCR) technology applied to historical printings in German Fraktur and Early Modern Latin.</p> <p>The ground truth comes in pairs of images of single printed lines as they appear in book pages (*.png) and their corresponding diplomatic transcriptions (*.gt.txt), which are UTF-8 strings preserving the character forms (glyphs) as much as possible within the UNICODE standard. These pairs of line images and their transcriptions can be directly used to train recognition models with, e.g., the open source OCR engines <em>OCRopy</em> or <em>Tesseract</em>. A total of 313,173 ground truth lines are provided.</p> <p><strong>Please note that the subcorpora making up this collection used different transcription guidelines, so it is a bad idea to train a recognition model on the total collection! Rather train individual models for each subcorpus.</strong> Fur further information about the subcorpora, please see the README file and the accompanying publication.</p> <p>If these data are useful for you, please cite the accompanying publication:</p> <pre>@article{<a href="http://springmann.net/publications.html#springmann2018gt4hist">springmann2018gt4hist</a>, author = {Uwe Springmann and Christian Reul and Stefanie Dipper and Johannes Baiter}, title = {{Ground Truth for training {OCR} engines on historical documents in German Fraktur and Early Modern Latin}}, journal = {J. Lang. Technol. Comput. Linguistics}, volume = {33}, number = {1}, pages = {97--114}, year = {2018}, url = {https://jlcl.org/content/2-allissues/1-heft1-2018/jlcl_2018-1_5.pdf} }</pre>
Evaluation Set for Historical Text Reuse Detection (HTRD) in 15th-Century German Prose Texts
<p>The evaluation set provides a pair of 15th-century German prose texts in which instances of assumed text reuse (TR) have been annotated, categorised and linked using the TEI standard. The annotated texts are intended to serve as evaluation set for computational methods and tools specialising on Historical Text Reuse Detection (HTRD). The texts featured in the evaluation set are (1) "Der Doernenkrantz van Collen", a late medieval lay didactic treatise on the estates of society, and (2) "Die Cronica van der hilliger Stat Coellen", one of the most important medieval chronicles of the city of Cologne. Both texts are written in Ripuarian, a west middle German dialect of the late middle ages and early modern period, and exhibit orthographic variance as well as transcription errors due to automatic Handwritten Text Recognition (HTR), which makes them challenging for HTRD.</p>
Historical German Children's Playbooks - 6 Digitized Books with Images, OCR-Fulltext, and Named Entity Recognition
<p>The dataset consists of 6 digitized books with 1750 images and OCR-fulltext.</p> <p>Additionally, named entity recognition has been carried out on basis of flair's de-ner model, see https://github.com/flairNLP for details.</p>
EncycNet: A Knowledge Graph of Historical German Encyclopedias
<p><strong>EncycNet</strong> is an automatically constructed knowledge graph that uses historical German encyclopedias as a data source. You can read more about the project here: <a href="https://encycnet.github.io/">https://encycnet.github.io/</a></p> <p>The first version of the graph (0.1) contains <em>Meyers Großes Konversations-Lexikon </em>(1905), with 5 more encyclopedias to be added in 2024. Formatted in RDF Turtle.</p> <p>The second version of the graph (0.2) contains 3 encyclopedias in total, meaning triples were changed to quads (see code example at <a href="https://github.com/EncycNet/Encyc-Relations">https://github.com/EncycNet/Encyc-Relations</a>):</p> <ul> <li><em>Meyers Großes Konversations-Lexikon </em>(1905)</li> <li><em>Herders Conversations-Lexikon</em> (1854)</li> <li><em>Brockhaus Bilder-Conversations-Lexikon</em> (1837)</li> </ul> <p>The second version also handled some bug fixes concerning wrongly matched Wikidata entries.</p> <p>The third version (0.3) now additionally contains</p> <ul> <li><em>Brockhaus Conversations-Lexikon oder kurzgefaßtes Handwörterbuch</em> (1809)</li> </ul> <p>It also impoved some namespace issues and introduced <a href="https://www.wikidata.org/wiki/Property:P8371">P8371</a> as encyclopedic refeferences in this case.</p>
DWUG DE Sense: A data set of historical word sense annotations in German
<p>This data collection contains a subset of <a href="https://zenodo.org/record/5543723">DWUG DE</a> word usage data annotated with classical word sense definitions (<em>DWUG DE Sense</em>, see <code>data/*/judgments_senses.csv</code>). From these annotations aggregated and cleaned sense labels were derived (<code>labels/*/labels_senses.csv</code>). From these labels we derived additional binary semantic proximity labels between use pairs ('0' for different sense, '1' for same sense, <code>labels/*/labels_proximity.csv</code>) and change labels reflecting sense changes between the two time periods from which word usages were sampled (<code>stats/*/stats_groupings.csv</code>).</p> <p>The sense labels were derived from the sense annotation by removing instances where not at least 2/3 annotators agree on the label (<code>maj_2</code>/<code>maj_3</code>). Note that the binary proximity labels were <em>derived</em> from the sense annotation, and not directly judged by humans (in contrast to other <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUG data sets</a>). Note that consequently also the change scores EARLIER, LATER and COMPARE were not calculated directly from human judgments, but from the inferred binary proximity labels. Please find the code aggregating and cleaning the data, deriving proximity labels and deriving change labels in the <a href="https://github.com/Garrafao/WUGs">WUG repository</a>.</p> <p>Please find more information on the provided data in the paper referenced below.</p> <p>Version: 1.0.1, 01.11.2024. Correct or remove some normalization and lemmatization errors in the uses. Updated references.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Frank D. Zamora-Reina, Felipe Bravo-Marquez, Nikolay Arefyev. 2024. <a href="https://doi.org/10.1007/s10579-024-09771-7">Sense Through Time: Diachronic Word Sense Annotations for Word Sense Induction and Lexical Semantic Change Detection</a>. Language Resources and Evaluation.</p> <p>Dominik Schlechtweg. 2023. <a href="http://dx.doi.org/10.18419/opus-12833">Human and Computational Measurement of Lexical Semantic Change</a>. PhD thesis. University of Stuttgart.</p>
Historically Accurate German Messer
Source: Objaverse 1.0 / Sketchfab
Retrospective Chart Review and Historical Comparison of Capmatinib vs. Standard of Care for German Adult Patients With Locally Advanced or Metastatic Non-small Cell Lung Cancer (NSCLC) Harboring METex
ClinicalTrials.gov study NCT05796726. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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