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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51 to 60 of 184 Results
roleVsNoRole.gold.txt
Sep 5, 2019 - Sentiment Compound Data (DE)
Plain Text - 68.6 KB - MD5: c8273f43236ed7f1b6de13d422997f30
Data
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
Real-World PP Attachment Disambiguation Dataset
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines, 2023, "Real-World PP Attachment Disambiguation Dataset", https://doi.org/10.11588/data/NB46XR, heiDATA, V1
This resource contains a German dataset for real-world PP attachment disambiguation. The creation, analysis and experiment results of the dataset are described in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited"
README_AbstractGraphs.txt
Jul 15, 2019 - Abstract graphs, abstract paths, grounded paths for Freebase and NELL
Plain Text - 2.7 KB - MD5: 4c073cf79f74569a44e3687f97b0be91
README.txt
Oct 8, 2019 - Affixoid Dataset (DE)
Plain Text - 758 B - MD5: 017f60a9c77782cd97a45c4dd74e117c
Documentation
readme.txt
Mar 26, 2020 - tweeDe
Plain Text - 4.3 KB - MD5: f331fd03061fbc1b28085934d6a9b10f
Documentation
readme.txt
Mar 26, 2020 - Converter for content-to-head style syntactic dependencies
Plain Text - 1.2 KB - MD5: fc57366f049837b691c85a50b3e47b46
Documentation
README.md
Sep 2, 2019 - Lexicon of Abusive Words (EN)
Markdown Text - 4.4 KB - MD5: 3cbbac5ff1534a6e9c3fcc9a1b0be976
Documentation
README.md
Sep 2, 2019 - GermEval-2018 Corpus (DE)
Markdown Text - 2.1 KB - MD5: 82583130d72db06eb5fe686c1a8338ac
Documentation
README.md
Jan 23, 2020 - Encoder-Decoder Model for Semantic Role Labeling
Markdown Text - 8.4 KB - MD5: 835ab4a78a83f8bea4d55dd6caa51837
Documentation
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