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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81 to 90 of 185 Results

README.md Nov 13, 2023 - Neural Rerankers for Dependency Parsing Markdown Text - 577 B - MD5: cfbb3eb52b0506ccc177b204a8be2577 Documentation
README.md Nov 13, 2023 - Neural PP Attachment Disambiguation Systems Markdown Text - 2.2 KB - MD5: a4c32be6d981dabdd2c2885b609042fa Documentation
README.md Nov 13, 2023 - Neural PP Attachment Disambiguation Systems Markdown Text - 285 B - MD5: edae4b18adbe165fcca7405d5fd14192 Documentation
README.md Nov 13, 2023 - Neural PP Attachment Disambiguation Systems Markdown Text - 1.3 KB - MD5: 11774e6d9660c9b5171e707d2d149175 Documentation
README.md Nov 13, 2023 - Head Selection Parsers and LSTM Labelers Markdown Text - 1.8 KB - MD5: aa0872f2f4fb632dd0a09b2faccb9927 Documentation
README.md Nov 13, 2023 - Head Selection Parsers and LSTM Labelers Markdown Text - 470 B - MD5: 0c88b76a2a9e9d2de5c2f0ceeb605fa9 Documentation
README.md Nov 13, 2023 - Head Selection Parsers and LSTM Labelers Markdown Text - 3.0 KB - MD5: 7cb511b7bf6510aa7c1f9f7ba47a98d8 Documentation
readme-pretrained.txt Mar 26, 2020 - Pre-trained POS tagging models for German social media Plain Text - 3.1 KB - MD5: 1a3740097d932694d280a13ddab6f2f4 Documentation
README Sep 2, 2019 - Opinion role extractor Plain Text - 13.0 KB - MD5: c4eb5b271a38da142c703216f9648f09 Documentation
README Jul 15, 2019 - ACL word segmentation correction Plain Text - 782 B - MD5: b305fd3ce016837f601aa137fd8ecf63 Documentation

README.md

Nov 13, 2023 - Neural Rerankers for Dependency Parsing

Markdown Text - 577 B -

Documentation

README.md

Nov 13, 2023 - Neural PP Attachment Disambiguation Systems

Markdown Text - 2.2 KB -

Documentation

README.md

Nov 13, 2023 - Neural PP Attachment Disambiguation Systems

Markdown Text - 285 B -

Documentation

README.md

Nov 13, 2023 - Neural PP Attachment Disambiguation Systems

Markdown Text - 1.3 KB -

Documentation

README.md

Nov 13, 2023 - Head Selection Parsers and LSTM Labelers

Markdown Text - 1.8 KB -

Documentation

README.md

Nov 13, 2023 - Head Selection Parsers and LSTM Labelers

Markdown Text - 470 B -

Documentation

README.md

Nov 13, 2023 - Head Selection Parsers and LSTM Labelers

Markdown Text - 3.0 KB -

Documentation

readme-pretrained.txt

Mar 26, 2020 - Pre-trained POS tagging models for German social media

Plain Text - 3.1 KB -

Documentation

README

Sep 2, 2019 - Opinion role extractor

Plain Text - 13.0 KB -

Documentation

README

Jul 15, 2019 - ACL word segmentation correction

Plain Text - 782 B -

Documentation

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