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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41 to 50 of 148 Results
lexicon-of-abusive-words-master.zip
Sep 2, 2019 - Lexicon of Abusive Words (EN)
ZIP Archive - 738.4 KB - MD5: 46f33f5b7a9c866b1a2fb6dc956b945d
LICENSE
Sep 5, 2019 - Sentiment View Lexicon (EN)
Plain Text - 18.2 KB - MD5: 4a17ffc27c9f3b240fbf4fe17783c89c
Documentation
LICENSE
Sep 5, 2019 - Sentiment Compound Data (DE)
Plain Text - 18.2 KB - MD5: 4a17ffc27c9f3b240fbf4fe17783c89c
Documentation
MACE-AL-TREE.zip
Mar 26, 2020 - MACE-AL-TREE
ZIP Archive - 141.9 KB - MD5: 7de327971177c2124d8f388a19b1c4c6
Code
MACE-AL.zip
Mar 26, 2020 - MACE-AL
ZIP Archive - 326.8 KB - MD5: 056a7e70a8f8b6e8fa72e3eead763d39
Code
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
Neural Techniques for German Dependency Parsing(Universität Heidelberg)
Nov 13, 2023
Research Data to the PhD Projects of Ngoc Do.
neural-tree-reranking.zip
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
ZIP Archive - 91.3 KB - MD5: 9940971e18bcec152cca698bbf71c0e8
Code
output.txt
Mar 26, 2020 - Pre-trained POS tagging models for German social media
Plain Text - 639 B - MD5: 1dc8fa318270ae3612341c0065889549
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