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
modal-sense-classifcation.zip
Oct 7, 2019 - Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]
ZIP Archive - 3.0 MB - MD5: 63c05670056bb1992a1e5ec370f0ccf3
MSC Data Set.zip
Oct 7, 2019 - The MSC Data Set
ZIP Archive - 6.2 MB - MD5: 98dbe1d608c24c3dfd31f166daeee77b
dataset_annotations.tab
Oct 8, 2019 - Affixoid Dataset (DE)
Tabular Data - 61.6 KB - 1 Variables, 1787 Observations - UNF:6:+MGK9lTPTXx7Rclu1BpPnw==
Data
README.txt
Oct 8, 2019 - Affixoid Dataset (DE)
Plain Text - 758 B - MD5: 017f60a9c77782cd97a45c4dd74e117c
Documentation
corec-master.zip
Oct 22, 2019 - COREC – A neural multi-label COmmonsense RElation Classification system
ZIP Archive - 6.2 KB - MD5: 04927f554601f39dd7e2d86a5e62d681
Code
RNN_for_Clause_Classification-master.zip
Oct 22, 2019 - Genre-sensitive Neural Situation Entity classifier (DE, EN)
ZIP Archive - 12.5 KB - MD5: 41d44420c6d5ea602e15e4140022af0f
Code
SitEnt_DE.zip
Dec 10, 2019 - GER_SET: Situation Entity Type labelled corpus for German
ZIP Archive - 414.5 KB - MD5: e1733e5ce7ef02577239d5a9ada0d8ba
README.md
Jan 23, 2020 - Encoder-Decoder Model for Semantic Role Labeling
Markdown Text - 8.4 KB - MD5: 835ab4a78a83f8bea4d55dd6caa51837
Documentation
SRL-S2S.zip
Jan 23, 2020 - Encoder-Decoder Model for Semantic Role Labeling
ZIP Archive - 42.5 MB - MD5: 5a525ee5066a01138845a1276c110956
Code
GTTC.tab
Mar 6, 2020 - German Twitter Titling Corpus
Tabular Data - 119.5 KB - 5 Variables, 1904 Observations - UNF:6:hDTAU0fvrPT3em851EVmhw==
Data
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