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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11 to 20 of 185 Results

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...
theano-biaffine-parser.zip Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention ZIP Archive - 36.0 KB - MD5: 3e8f69e918c003c92700af524474ad31 Code
The MSC Data Set Oct 7, 2019 Marasović, Ana; Zhou, Mengfei; Frank, Anette, 2019, "The MSC Data Set", https://doi.org/10.11588/data/JEESIQ, heiDATA, V1 From this page you can download resources we created for modal sense classification as reported in Zhou et al. (2015), Marasović et al. (2016) and Marasović and Frank (2015) (see "Related Publication" below): Heuristically sense-annotated training data acquired from EUROPARL and...
supplementaryNotes.pdf Sep 5, 2019 - Sentiment Compound Data (DE) Adobe PDF - 126.2 KB - MD5: 846a2849d5f0f4a119d504d79260c6fa Documentation
SRL-S2S.zip Jan 23, 2020 - Encoder-Decoder Model for Semantic Role Labeling ZIP Archive - 42.5 MB - MD5: 5a525ee5066a01138845a1276c110956 Code
spmrl.tar.gz Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention Gzip Archive - 361.8 MB - MD5: c31097376ca9d2b08c013fcdbba10c6d Data
spmrl.tar.gz Nov 13, 2023 - Head Selection Parsers and LSTM Labelers Gzip Archive - 47.1 MB - MD5: 29fe91de0ec97d2282ebdac310e66e9d Data
spmrl-word.tar.gz Nov 13, 2023 - Head Selection Parsers and LSTM Labelers Gzip Archive - 32.4 MB - MD5: a7fa938e3000c7e0427ef3c2b3ec8d28 Data
spmrl-word-pos.tar.gz Nov 13, 2023 - Head Selection Parsers and LSTM Labelers Gzip Archive - 32.5 MB - MD5: 2d06256605fad989aa18b00e46922463 Data
spmrl-word-comp.tar.gz Nov 13, 2023 - Head Selection Parsers and LSTM Labelers Gzip Archive - 68.0 MB - MD5: a362283a76428bb053c6f59a928b3a8d Data

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...

theano-biaffine-parser.zip

Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention

ZIP Archive - 36.0 KB -

Code

The MSC Data Set

Oct 7, 2019

Marasović, Ana; Zhou, Mengfei; Frank, Anette, 2019, "The MSC Data Set", https://doi.org/10.11588/data/JEESIQ, heiDATA, V1

From this page you can download resources we created for modal sense classification as reported in Zhou et al. (2015), Marasović et al. (2016) and Marasović and Frank (2015) (see "Related Publication" below): Heuristically sense-annotated training data acquired from EUROPARL and...

supplementaryNotes.pdf

Sep 5, 2019 - Sentiment Compound Data (DE)

Adobe PDF - 126.2 KB -

Documentation

SRL-S2S.zip

Jan 23, 2020 - Encoder-Decoder Model for Semantic Role Labeling

ZIP Archive - 42.5 MB -

Code

spmrl.tar.gz

Nov 13, 2023 - Neural Dependency Parser with Biaffine Attention

Gzip Archive - 361.8 MB -

Data

spmrl.tar.gz

Nov 13, 2023 - Head Selection Parsers and LSTM Labelers

Gzip Archive - 47.1 MB -

Data

spmrl-word.tar.gz

Nov 13, 2023 - Head Selection Parsers and LSTM Labelers

Gzip Archive - 32.4 MB -

Data

spmrl-word-pos.tar.gz

Nov 13, 2023 - Head Selection Parsers and LSTM Labelers

Gzip Archive - 32.5 MB -

Data

spmrl-word-comp.tar.gz

Nov 13, 2023 - Head Selection Parsers and LSTM Labelers

Gzip Archive - 68.0 MB -

Data

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