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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71 to 80 of 185 Results
README.md
Nov 13, 2023 - Real-World PP Attachment Disambiguation Dataset
Markdown Text - 1.3 KB - MD5: c568b46eabbae41d24c31490cbb32e8b
Documentation
spmrl-marmot-biaffine.tar.gz
Nov 13, 2023 - Real-World PP Attachment Disambiguation Dataset
Gzip Archive - 10.5 MB - MD5: c9feffe0c9f1c9a3116180f13a6acd0b
Data
spmrl-pp-gold-obj-dekok.tar.gz
Nov 13, 2023 - Real-World PP Attachment Disambiguation Dataset
Gzip Archive - 4.8 MB - MD5: 0b2da98b084ed8634f943174e6ade059
Data
spmrl-pp-gold-obj-jsonl.tar.gz
Nov 13, 2023 - Real-World PP Attachment Disambiguation Dataset
Gzip Archive - 25.2 MB - MD5: 61a498ef8214fa06dae3abfabaafde5d
Data
spmrl-pp-pred-obj-dekok.tar.gz
Nov 13, 2023 - Real-World PP Attachment Disambiguation Dataset
Gzip Archive - 5.5 MB - MD5: 1d7f9fc905b5a7cbe27cd0bce3596815
Data
spmrl-pp-pred-obj-jsonl.tar.gz
Nov 13, 2023 - Real-World PP Attachment Disambiguation Dataset
Gzip Archive - 25.2 MB - MD5: 7580da3efb7f67cfc245c358725e74ef
tubadz-pp-aux.tar.gz
Nov 13, 2023 - Real-World PP Attachment Disambiguation Dataset
Gzip Archive - 4.3 MB - MD5: b37e0268b451b32e52948e47baf80603
Data
tubadz-pp.tar.gz
Nov 13, 2023 - Real-World PP Attachment Disambiguation Dataset
Gzip Archive - 1.7 MB - MD5: b2d04463fd249e1a19e641a99c65e70d
Data
Neural Dependency Parser with Biaffine Attention and BERT Embeddings
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines, 2023, "Neural Dependency Parser with Biaffine Attention and BERT Embeddings", https://doi.org/10.11588/data/0U6IWL, heiDATA, V1
This resource contains the code of the dependency parser used in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017) and is extended to use the BERT...
biaffine-parser.zip
Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention and BERT Embeddings
ZIP Archive - 46.4 KB - MD5: 727dde9bcf6285b968ebbccc5459674b
Code
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