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 Language (Leibniz-Institut für Deutsche Sprache, IDS) in Mannheim and the Department of Computational Linguistics at Heidelberg University (ICL). The general aims of the project are to develop new methods, models, and tools for compiling and analysing automatically large German textual corpora covering different domains, genres and language varieties.

The project is supported by funds from the Baden-Württemberg Ministry of Science, Research and the Arts and the Leibniz Association together with funds provided by the Leibniz Institute for the German Language and Heidelberg University.

Funding Period: 2015 – 2020

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11 to 20 of 185 Results
AbstractGraphs_Garder2014data.tar.gz
Jul 15, 2019 - Abstract graphs, abstract paths, grounded paths for Freebase and NELL
Gzip Archive - 2.5 GB - MD5: dc2a64fb2d88cccf5e62d9400cbca1af
Data
AbstractPathsGroundedPaths.tar.gz
Jul 15, 2019 - Abstract graphs, abstract paths, grounded paths for Freebase and NELL
Gzip Archive - 204.7 MB - MD5: 6dfafe4e7d5b29a882b59f43ec9eb4ae
Data
README_AbstractGraphs.txt
Jul 15, 2019 - Abstract graphs, abstract paths, grounded paths for Freebase and NELL
Plain Text - 2.7 KB - MD5: 4c073cf79f74569a44e3687f97b0be91
KGE Algorithms
Aug 19, 2019
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...
kge-rl-master.zip
Aug 19, 2019 - KGE Algorithms
ZIP Archive - 19.4 KB - MD5: d2e8ac74e3f20d2cdec2225962c7e2f0
Code
Negative Sampling for Learning Knowledge Graph Embeddings
Aug 19, 2019
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...
kge-rl-master.zip
Aug 19, 2019 - Negative Sampling for Learning Knowledge Graph Embeddings
ZIP Archive - 19.4 KB - MD5: d2e8ac74e3f20d2cdec2225962c7e2f0
Code
Twitter Titling Corpus
Aug 23, 2019
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...
twitter_titling_corpus.tab
Aug 23, 2019 - Twitter Titling Corpus
Tabular Data - 219.0 KB - 5 Variables, 4002 Observations - UNF:6:+F3lLKziwMvjy+xyktkilw==
Data
Opinion role extractor
Sep 2, 2019
Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1
System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text
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