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Feb 17, 2021 - Empirical Linguistics and Computational Language Modeling (LiMo)
Daza, Angel, 2021, "X-SRL Dataset and mBERT Word Aligner", https://doi.org/10.11588/data/HVXXIJ, heiDATA, V1
This code contains a method to automatically align words from parallel sentences by using multilingual BERT pre-trained embeddings. This can be used to transfer source annotations (for example labeled English sentences) into the target side (for example a German translation of th...
Aug 13, 2014 - Database Systems Research Group
Strötgen, Jannik; Gertz, Michael, 2014, "WikiWarsDE Corpus", https://doi.org/10.11588/data/10026, heiDATA, V1
The WikiWarsDE corpus is a German corpus containing Wikipedia articles with annotations of temporal expressions. Its creation was motivated by the English WikiWars corpus (Mazur & Dale 2010). WikiWarsDE was developed to support research on temporal information extraction and norm...
Jun 18, 2014 - Statistical Natural Language Processing Group
Hieber, Felix; Schamoni, Shigehiko; Sokolov, Artem; Riezler, Stefan, 2014, "WikiCLIR: A Cross-Lingual Retrieval Dataset from Wikipedia", https://doi.org/10.11588/data/10003, heiDATA, V1
WikiCLIR is a large-scale (German-English) retrieval data set for Cross-Language Information Retrieval (CLIR). It contains a total of 245,294 German single-sentence queries with 3,200,393 automatically extracted relevance judgments for 1,226,741 English Wikipedia articles as docu...
Aug 23, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
van den Berg, Esther; Korfhage, Katharina; Ruppenhofer, Josef; Wiegand, Michael; Markert, Katja, 2019, "Twitter Titling Corpus", https://doi.org/10.11588/data/IOHXDF, heiDATA, V1, UNF:6:+F3lLKziwMvjy+xyktkilw== [fileUNF]
The Twitter Titling Corpus contains 4002 stance-annotated tweets collected between 20 June 2017 and 30 August 2017 mentioning 6 presidents. Each tweet is annotated for the naming form used to refer to the president, for the purpose of a study on the relation between naming variat...
Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
Rehbein, Ines; Ruppenhofer, Josef; Do, Bich-Ngoc, 2020, "tweeDe", https://doi.org/10.11588/data/S90S35, heiDATA, V1
A German UD Twitter treebank, with >12,000 tokens from 519 tweets, annotated in the Universal Dependencies framework
Theoretical Physics(Heidelberg University - Institute of Theoretical Physics)
Oct 15, 2014
This Dataverse contains research data of the Institute for Theoretical Physics at Heidelberg University.
Oct 7, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Marasović, Ana; Zhou, Mengfei; Frank, Anette, 2019, "The MSC Data Set", https://doi.org/10.11588/data/JEESIQ, heiDATA, V1
From this page you can download resources we created for modal sense classification as reported in Zhou et al. (2015), Marasović et al. (2016) and Marasović and Frank (2015) (see "Related Publication" below): Heuristically sense-annotated training data acquired from EUROPARL and...
Nov 2, 2016 - Perspektive Bibliothek
Drees, Bastian, 2016, "Text und Data Mining an wissenschaftlichen Repositorien und Publikationsservern in Deutschland - Zusammenfassung der Ergebnisse einer Umfrage im Februar und März 2016", https://doi.org/10.11588/data/10090, heiDATA, V2
Es wurden die auf den Homepages angegebenen Ansprechpartner wissenschaftlicher Repositorien und Publikationsserver in Deutschland zu ihren Erfahrungen mit Text und Data Mining befragt. Die Befragung fand zwischen dem 22. und 26.2.2016 per E-Mail statt. Es wurden Ansprechpartner v...
Jul 25, 2022 - Scientific Software Center (SSC)
Uieda, Leonardo, 2022, "Test data for the Pooch library", https://doi.org/10.11588/data/TKCFEF, heiDATA, V1
Pooch is an open-source Python library for data download. This archive contains testing data for Pooch's DataVerse download functionality.
Jul 14, 2022 - Computer Assisted Clinical Medicine
Zöllner, Frank, 2022, "Synthesis of CT images from digital body phantoms using CycleGAN [dataset]", https://doi.org/10.11588/data/7NRFYC, heiDATA, V1
The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data...
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