Skip to main content
Share Dataverse

Share this dataverse on your favorite social media networks.

Metrics
20,565 Downloads
heiDATA is an institutional repository for research data of Heidelberg University. It is managed by the Competence Centre for Research Data, a joint institution of the University Library and the Computing Centre. If you are interested in publishing your data here, please see our author instructions and get in touch with us.
Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Find Advanced Search

1 to 10 of 218 Results
Aug 23, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
van den Berg, Esther, 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...
Aug 19, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Kotnis, Bhushan, 2019, "Negative Sampling for Learning Knowledge Graph Embeddings", https://doi.org/10.11588/data/YYULL2, heiDATA, V1
Reimplementation of four KG factorization methods and six negative sampling methods. Abstract Knowledge graphs are large, useful, but incomplete knowledge repositories. They encode knowledge through entities and relations which define each other through the connective structure o...
Aug 19, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Kotnis, Bhushan, 2019, "KGE Algorithms", https://doi.org/10.11588/data/CSXYSS, heiDATA, V1
An updated method for link prediction that uses a regularization factor that models relation argument types Abstract (Kotnis and Nastase, 2017): Learning relations based on evidence from knowledge repositories relies on processing the available relation instances. Knowledge repos...
Jul 15, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Nastase, Vivi; Kotnis, Bhushan, 2019, "Abstract graphs, abstract paths, grounded paths for Freebase and NELL", https://doi.org/10.11588/data/AVLFPZ, heiDATA, V1
We describe a method for representing knowledge graphs that capture an intensional representation of the original extensional information. This representation is very compact, and it abstracts away from individual links, allowing us to find better path candidates, as shown by the...
Jul 15, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Nastase, Vivi; Hitschler, Julian, 2019, "ACL word segmentation correction", https://doi.org/10.11588/data/VK99LU, heiDATA, V1
The data in this collection consists of two parallel directories, one ("raw") containing the raw text of 18850 articles from the ACL 2013/02 collection, the other ("re-segmented") the word-resegmented version of these articles, obtained using nematus, a seq2seq neural model used...
Jul 15, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Nastase, Vivi; Fritz, Devon; Frank, Anette, 2019, "DeModify", https://doi.org/10.11588/data/KIWEMF, heiDATA, V1
deModify consists of 3631 instances, each with three annotations obtained through CrowdFlower. An instance is a short story in which a modifier is annotated with respect to its impact on the information in the story, assessed through its deletion from the context: crucial, not-cr...
Jul 12, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Opitz, Juri, 2019, "AMR parse quality prediction [Source Code]", https://doi.org/10.11588/data/STHBGW, heiDATA, V1
Accuracy prediction for AMR parsing predicts 33 accuracy metrics for a given sentence and its (automatic) AMR parse Abstract (Opitz and Frank, 2019): Semantic proto-role labeling (SPRL) is an alternative to semantic role labeling (SRL) that moves beyond a categorical definition o...
Empirical Linguistics and Computational Language Modeling (LiMo)(Department of Computational Linguistics of Heidelberg University and Leibniz Institute for the German Language)
Jul 12, 2019
Data publications of the Leibniz ScienceCampus “Empirical Linguistics and Computational Language Modeling” The Leibniz ScienceCampus “Empirical Linguistics and Computational Language Modeling” (LiMo) is a cooperative research project between the Leibniz Institute for the German L...
Jul 2, 2019 - Propylaeum
Höke, Benjamin; Gauß, Florian; Peek, Christina; Stelzner, Jörg, 2019, "Lauchheim II.2. Katalog der Gräber 301–600", https://doi.org/10.11588/data/HB97MY, heiDATA, V1
Mit rund 1300 Gräbern aus dem Zeitraum vom späten 5. bis zum späten 7. Jahrhundert ist das Gräberfeld von Lauchheim ‚Wasserfurche‘ (Ostalbkreis) bis heute der größte bekannte merowingerzeitliche Bestattungsplatz Süddeutschlands. In den Jahren 1986 bis 1996 wurde das fast vollstän...
Jun 28, 2019 - SFB 933 Materiale Textkulturen - Teilprojekt C05
Ott, Michael R., 2019, "Erzählte Inschriften in der Literatur des Mittelalters (Projektdatenbank)", https://doi.org/10.11588/data/0HJAJS, heiDATA, V2, UNF:6:zYyA6vs0VkcR2qiIHbGcVw== [fileUNF]
Diese Datenpublikation entstammt dem Teilprojekt C05 (»Inschriftlichkeit. Reflexionen materialer Textkultur in der Literatur des 12. bis 17. Jahrhunderts«) des Sonderforschungsbereichs 933 (»Materiale Textkulturen«). Im Rahmen des Teilprojekts werden erzählte Inschriften in der m...
Add Data

Sign up or log in to create a dataverse or add a dataset.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.

Contact heiDATA Support

heiDATA Support

Please fill this out to prove you are not a robot.

+ =
Send Message