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

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

41 to 50 of 185 Results
embeddings.tar.gz
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
Gzip Archive - 93.1 MB - MD5: f2ed8e0973b3953701d0193e597737b4
Data
en-gcn.tar.gz
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
Gzip Archive - 23.8 MB - MD5: a87f5b576b0c021a0801ebc8e6c83d7c
Data
en-rcnn-shared.tar.gz
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
Gzip Archive - 17.8 MB - MD5: 0697f8c0662f6548b6dd06b1680a09df
Data
en-rcnn.tar.gz
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
Gzip Archive - 113.7 MB - MD5: 81c3265a0c8b9fbe73f790d353289a77
Data
neural-tree-reranking.zip
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
ZIP Archive - 91.3 KB - MD5: 9940971e18bcec152cca698bbf71c0e8
Code
README.md
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
Markdown Text - 1.5 KB - MD5: 9b99b461dc66b9e2d63bd143ffa13919
Documentation
README.md
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
Markdown Text - 82 B - MD5: 6f6c9146cb4bb5767db27672dcc6103f
Documentation
README.md
Nov 13, 2023 - Neural Rerankers for Dependency Parsing
Markdown Text - 577 B - MD5: cfbb3eb52b0506ccc177b204a8be2577
Documentation
Tool for Extracting PP Attachment Disambiguation Dataset
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines, 2023, "Tool for Extracting PP Attachment Disambiguation Dataset", https://doi.org/10.11588/data/RHD3KS, heiDATA, V1
This resource contains code to extract a PP attachment disambiguation dataset as described in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited". The input is in CoNLL format, and the output format is similar to the one described in de Kok et al...
pp-attachment-candidate-extraction.zip
Nov 13, 2023 - Tool for Extracting PP Attachment Disambiguation Dataset
ZIP Archive - 11.6 KB - MD5: 9fb938cbabb83b7434f5e790be72c80f
CodeData
Add Data

Sign up or log in to create a dataverse or add a dataset.

Sign Up Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.