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 38 Results

Sentiment View Lexicon (EN) Sep 5, 2019 Wiegand, Michael; Ruppenhofer, Josef; Schulder, Marc, 2019, "Sentiment View Lexicon (EN)", https://doi.org/10.11588/data/2JK48O, heiDATA, V1 This gold standard contains sentiment expressions (verbs, nouns and adjectives) that have been annotated according to their (prior) sentiment view. Each sentiment expression is labelled either as actor or speaker view.
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.
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...
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...
Affixoid Dataset (DE) Oct 8, 2019 Ruppenhofer, Josef, 2019, "Affixoid Dataset (DE)", https://doi.org/10.11588/data/QKF4LT, heiDATA, V1, UNF:6:+MGK9lTPTXx7Rclu1BpPnw== [fileUNF] The dataset contains the manual annotations for the COLING 2018 submission "Distinguishing affixoid formations from compounds" by Josef Ruppenhofer, Michael Wiegand, Rebecca Wilm and Katja Markert. 1788 complex words containing one of 7 German suffixoid candidates (e.g. -hai, -go...
COREC – A neural multi-label COmmonsense RElation Classification system Oct 22, 2019 Becker, Maria, 2019, "COREC – A neural multi-label COmmonsense RElation Classification system", https://doi.org/10.11588/data/E5EHBV, heiDATA, V1 We examine the learnability of Commonsense knowledge relations as represented in CONCEPTNET. We develop a neural open world multi-label classification system that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the spec...
Genre-sensitive Neural Situation Entity classifier (DE, EN) Oct 22, 2019 Becker, Maria, 2019, "Genre-sensitive Neural Situation Entity classifier (DE, EN)", https://doi.org/10.11588/data/XXKWU0, heiDATA, V1 This is a Classifier for situation entity types as described in Becker et al., 2017. These clause types depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, synta...
GER_SET: Situation Entity Type labelled corpus for German Dec 10, 2019 Becker, Maria, 2019, "GER_SET: Situation Entity Type labelled corpus for German", https://doi.org/10.11588/data/BBQYD0, heiDATA, V1 Semantic clause types, also called Situation Entity (SE) types (Smith, 2003) are linguistic characterizations of aspectual properties shown to be useful for tasks like argumentation structure analysis (Becker et al., 2016), genre characterization (Palmer and Friedrich, 2014), and...
Encoder-Decoder Model for Semantic Role Labeling Jan 23, 2020 Daza, Angel, 2020, "Encoder-Decoder Model for Semantic Role Labeling", https://doi.org/10.11588/data/TOI9NQ, heiDATA, V1 Abstract (Daza & Frank 2019): We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need paral...
German causal language annotations and lexicon (verbs, nouns, prepositions) (DE) Mar 26, 2020 Rehbein, Ines; Ruppenhofer, Josef, 2020, "German causal language annotations and lexicon (verbs, nouns, prepositions) (DE)", https://doi.org/10.11588/data/ZHI94V, heiDATA, V1 Annotations of causal verbs, nouns and prepositions in context and lexicon file for causal verbs, nouns and prepositions.

Sentiment View Lexicon (EN)

Sep 5, 2019

Wiegand, Michael; Ruppenhofer, Josef; Schulder, Marc, 2019, "Sentiment View Lexicon (EN)", https://doi.org/10.11588/data/2JK48O, heiDATA, V1

This gold standard contains sentiment expressions (verbs, nouns and adjectives) that have been annotated according to their (prior) sentiment view. Each sentiment expression is labelled either as actor or speaker view.

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.

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...

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...

Affixoid Dataset (DE)

Oct 8, 2019

Ruppenhofer, Josef, 2019, "Affixoid Dataset (DE)", https://doi.org/10.11588/data/QKF4LT, heiDATA, V1, UNF:6:+MGK9lTPTXx7Rclu1BpPnw== [fileUNF]

The dataset contains the manual annotations for the COLING 2018 submission "Distinguishing affixoid formations from compounds" by Josef Ruppenhofer, Michael Wiegand, Rebecca Wilm and Katja Markert. 1788 complex words containing one of 7 German suffixoid candidates (e.g. -hai, -go...

COREC – A neural multi-label COmmonsense RElation Classification system

Oct 22, 2019

Becker, Maria, 2019, "COREC – A neural multi-label COmmonsense RElation Classification system", https://doi.org/10.11588/data/E5EHBV, heiDATA, V1

We examine the learnability of Commonsense knowledge relations as represented in CONCEPTNET. We develop a neural open world multi-label classification system that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the spec...

Genre-sensitive Neural Situation Entity classifier (DE, EN)

Oct 22, 2019

Becker, Maria, 2019, "Genre-sensitive Neural Situation Entity classifier (DE, EN)", https://doi.org/10.11588/data/XXKWU0, heiDATA, V1

This is a Classifier for situation entity types as described in Becker et al., 2017. These clause types depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, synta...

GER_SET: Situation Entity Type labelled corpus for German

Dec 10, 2019

Becker, Maria, 2019, "GER_SET: Situation Entity Type labelled corpus for German", https://doi.org/10.11588/data/BBQYD0, heiDATA, V1

Semantic clause types, also called Situation Entity (SE) types (Smith, 2003) are linguistic characterizations of aspectual properties shown to be useful for tasks like argumentation structure analysis (Becker et al., 2016), genre characterization (Palmer and Friedrich, 2014), and...

Encoder-Decoder Model for Semantic Role Labeling

Jan 23, 2020

Daza, Angel, 2020, "Encoder-Decoder Model for Semantic Role Labeling", https://doi.org/10.11588/data/TOI9NQ, heiDATA, V1

Abstract (Daza & Frank 2019): We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need paral...

German causal language annotations and lexicon (verbs, nouns, prepositions) (DE)

Mar 26, 2020

Rehbein, Ines; Ruppenhofer, Josef, 2020, "German causal language annotations and lexicon (verbs, nouns, prepositions) (DE)", https://doi.org/10.11588/data/ZHI94V, heiDATA, V1

Annotations of causal verbs, nouns and prepositions in context and lexicon file for causal verbs, nouns and prepositions.

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