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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141 to 150 of 184 Results

Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code] Oct 7, 2019 Marasović, Ana, 2019, "Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]", https://doi.org/10.11588/data/ERDJDI, heiDATA, V1 Abstract Modal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal’s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based cl...
modal-sense-classifcation.zip Oct 7, 2019 - Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code] ZIP Archive - 3.0 MB - MD5: 63c05670056bb1992a1e5ec370f0ccf3
Sentiment Compound Data (DE) Sep 5, 2019 Wiegand, Michael; Bocionek, Christine; Ruppenhofer, Josef, 2019, "Sentiment Compound Data (DE)", https://doi.org/10.11588/data/LSTRK3, heiDATA, V1 This dataset contains gold standards that are required for building a classifier that automatically extracts opinion (noun) compounds.
holderVsTarget.gold.txt Sep 5, 2019 - Sentiment Compound Data (DE) Plain Text - 34.6 KB - MD5: 13ac9f60aa9ba2fbb42d0b9d2b9f6e2f Data
LICENSE Sep 5, 2019 - Sentiment Compound Data (DE) Plain Text - 18.2 KB - MD5: 4a17ffc27c9f3b240fbf4fe17783c89c Documentation
README Sep 5, 2019 - Sentiment Compound Data (DE) Plain Text - 3.4 KB - MD5: 5900f0947dba284902650ebd6b5fb2a6 Documentation
README Sep 5, 2019 - Sentiment Compound Data (DE) Plain Text - 4.1 KB - MD5: a9954351d3510f45cfead5e27dd7060a Documentation
roleVsNoRole.gold.txt Sep 5, 2019 - Sentiment Compound Data (DE) Plain Text - 68.6 KB - MD5: c8273f43236ed7f1b6de13d422997f30 Data
sentimentViewsOfOpinionNouns.txt Sep 5, 2019 - Sentiment Compound Data (DE) Plain Text - 31.9 KB - MD5: bf369686743f258705fd6cc675cfcaf0 Data
supplementaryNotes.pdf Sep 5, 2019 - Sentiment Compound Data (DE) Adobe PDF - 126.2 KB - MD5: 846a2849d5f0f4a119d504d79260c6fa Documentation

Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]

Oct 7, 2019

Marasović, Ana, 2019, "Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]", https://doi.org/10.11588/data/ERDJDI, heiDATA, V1

Abstract Modal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal’s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based cl...

modal-sense-classifcation.zip

Oct 7, 2019 - Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]

ZIP Archive - 3.0 MB -

Sentiment Compound Data (DE)

Sep 5, 2019

Wiegand, Michael; Bocionek, Christine; Ruppenhofer, Josef, 2019, "Sentiment Compound Data (DE)", https://doi.org/10.11588/data/LSTRK3, heiDATA, V1

This dataset contains gold standards that are required for building a classifier that automatically extracts opinion (noun) compounds.

holderVsTarget.gold.txt