Empirical Linguistics and Computational Language Modeling (LiMo)

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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1 to 10 of 185 Results

Neural Dependency Parser with Biaffine Attention Nov 13, 2023 - Neural Techniques for German Dependency Parsing Fankhauser, Peter; Do, Bich-Ngoc; Kupietz, Marc, 2023, "Neural Dependency Parser with Biaffine Attention", https://doi.org/10.11588/data/DZ9MUS, heiDATA, V1 This resource contains the code of the dependency parser used in the paper: Fankhauser, et al. (2020). "Evaluating a Dependency Parser on DeReKo". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017). In addition, we include two pre-trai...
README.md Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention Markdown Text - 1.8 KB - MD5: 5b149f86795cd84547379060d779cc11 Documentation
README.md Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention Markdown Text - 232 B - MD5: a8b24a0d64ab866f78a8b474344acfcf Documentation
spmrl-marmot.tar.gz Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention Gzip Archive - 361.9 MB - MD5: a40f342adfcff8bf8217d6c7da8c4082 Data
spmrl.tar.gz Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention Gzip Archive - 361.8 MB - MD5: c31097376ca9d2b08c013fcdbba10c6d Data
theano-biaffine-parser.zip Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention ZIP Archive - 36.0 KB - MD5: 3e8f69e918c003c92700af524474ad31 Code
Datasets for Dependency Tree Reranking Nov 13, 2023 - Neural Techniques for German Dependency Parsing Do, Bich-Ngoc; Rehbein, Ines, 2023, "Datasets for Dependency Tree Reranking", https://doi.org/10.11588/data/E5NOYH, heiDATA, V1 This resource contains the datasets for dependency tree reranking in 3 languages: English, German and Czech. The creation, analysis and experiment results of the datasets are described in the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation".
cs-ud-kbest50.tar.gz Nov 13, 2023 - Datasets for Dependency Tree Reranking Gzip Archive - 54.3 MB - MD5: e1df54b815eec985f102d48a31426107 Data
cs-ud-kbest50-top10.tar.gz Nov 13, 2023 - Datasets for Dependency Tree Reranking Gzip Archive - 21.1 MB - MD5: ceed7b0509f5d95f9d6a5003229cf770 Data
de-spmrl-kbest50.tar.gz Nov 13, 2023 - Datasets for Dependency Tree Reranking Gzip Archive - 32.4 MB - MD5: bcaf176e4f2a5b396441afb78ae698d7 Data

Neural Dependency Parser with Biaffine Attention

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Fankhauser, Peter; Do, Bich-Ngoc; Kupietz, Marc, 2023, "Neural Dependency Parser with Biaffine Attention", https://doi.org/10.11588/data/DZ9MUS, heiDATA, V1

This resource contains the code of the dependency parser used in the paper: Fankhauser, et al. (2020). "Evaluating a Dependency Parser on DeReKo". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017). In addition, we include two pre-trai...

README.md

Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention

Markdown Text - 1.8 KB -

Documentation

README.md

Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention

Markdown Text - 232 B -

Documentation

spmrl-marmot.tar.gz

Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention

Gzip Archive - 361.9 MB -

Data

spmrl.tar.gz

Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention

Gzip Archive - 361.8 MB -

Data

theano-biaffine-parser.zip

Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention

ZIP Archive - 36.0 KB -

Code

Datasets for Dependency Tree Reranking

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Do, Bich-Ngoc; Rehbein, Ines, 2023, "Datasets for Dependency Tree Reranking", https://doi.org/10.11588/data/E5NOYH, heiDATA, V1

This resource contains the datasets for dependency tree reranking in 3 languages: English, German and Czech. The creation, analysis and experiment results of the datasets are described in the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation".

cs-ud-kbest50.tar.gz

Nov 13, 2023 - Datasets for Dependency Tree Reranking

Gzip Archive - 54.3 MB -

Data

cs-ud-kbest50-top10.tar.gz

Nov 13, 2023 - Datasets for Dependency Tree Reranking

Gzip Archive - 21.1 MB -

Data

de-spmrl-kbest50.tar.gz

Nov 13, 2023 - Datasets for Dependency Tree Reranking

Gzip Archive - 32.4 MB -

Data

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