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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1 to 10 of 57 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
acl-201302_word-resegmented.tar.gz
Jul 15, 2019 - ACL word segmentation correction
Gzip Archive - 371.1 MB - MD5: 96d089771cde56bb9ac5296189fb403b
Data
text files
causal_language_DE_release1.0.zip
Mar 26, 2020 - German causal language annotations and lexicon (verbs, nouns, prepositions) (DE)
ZIP Archive - 1.6 MB - MD5: f928beb9f56c4a3e011941904872a4eb
Data
convert.zip
Mar 26, 2020 - Converter for content-to-head style syntactic dependencies
ZIP Archive - 10.1 MB - MD5: 30167cb475d743ced8aa63e6349a99ce
CodeDocumentation
corec-master.zip
Oct 22, 2019 - COREC – A neural multi-label COmmonsense RElation Classification system
ZIP Archive - 6.2 KB - MD5: 04927f554601f39dd7e2d86a5e62d681
Code
dataset_annotations.tab
Oct 8, 2019 - Affixoid Dataset (DE)
Tabular Data - 61.6 KB - 1 Variables, 1787 Observations - UNF:6:+MGK9lTPTXx7Rclu1BpPnw==
Data
demodify.data_split.tsv
Jul 15, 2019 - DeModify
Tab-Separated Values - 112.2 KB - MD5: 9859efc83ee0b6a30af19448be4d6f0b
Data
demodify.tsv
Jul 15, 2019 - DeModify
Tab-Separated Values - 5.1 MB - MD5: 12bab5c05a384c4fbe64c9afd81f9c6d
Data
example.txt
Mar 26, 2020 - Pre-trained POS tagging models for German social media
Plain Text - 333 B - MD5: fef85f2d0d0a34d965014646659e5222
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