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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31 to 40 of 147 Results
en-rcnn.tar.gz
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
Gzip Archive - 113.7 MB - MD5: 81c3265a0c8b9fbe73f790d353289a77
Data
example.txt
Mar 26, 2020 - Pre-trained POS tagging models for German social media
Plain Text - 333 B - MD5: fef85f2d0d0a34d965014646659e5222
german-opinon-role-extractor-master.zip
Sep 2, 2019 - Opinion role extractor
ZIP Archive - 20.8 MB - MD5: 6704c06c5a8566eb05c3a8e0e0baebc2
Code
GermEval-2018-Data-master.zip
Sep 2, 2019 - GermEval-2018 Corpus (DE)
ZIP Archive - 14.8 MB - MD5: 6471a35acf802906383e6d19e5241b37
Code
GTTC.tab
Mar 6, 2020 - German Twitter Titling Corpus
Tabular Data - 119.5 KB - 5 Variables, 1904 Observations - UNF:6:hDTAU0fvrPT3em851EVmhw==
Data
GTTC_addendum.tab
Jan 20, 2021 - German Twitter Titling Corpus
Tabular Data - 19.7 KB - 5 Variables, 296 Observations - UNF:6:e8JLFj0rmt8hCbrLS38QTg==
Data
holderVsTarget.gold.txt
Sep 5, 2019 - Sentiment Compound Data (DE)
Plain Text - 34.6 KB - MD5: 13ac9f60aa9ba2fbb42d0b9d2b9f6e2f
Data
hunpos-social-media.model.bz2
Mar 26, 2020 - Pre-trained POS tagging models for German social media
Bzip Archive - 6.0 MB - MD5: 130e09643a6ec5b26bcdf520571f261d
Data
kge-rl-master.zip
Aug 19, 2019 - Negative Sampling for Learning Knowledge Graph Embeddings
ZIP Archive - 19.4 KB - MD5: d2e8ac74e3f20d2cdec2225962c7e2f0
Code
kge-rl-master.zip
Aug 19, 2019 - KGE Algorithms
ZIP Archive - 19.4 KB - MD5: d2e8ac74e3f20d2cdec2225962c7e2f0
Code
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