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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51 to 60 of 185 Results

corec-master.zip Oct 22, 2019 - COREC – A neural multi-label COmmonsense RElation Classification system ZIP Archive - 6.2 KB - MD5: 04927f554601f39dd7e2d86a5e62d681 Code
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...
RNN_for_Clause_Classification-master.zip Oct 22, 2019 - Genre-sensitive Neural Situation Entity classifier (DE, EN) ZIP Archive - 12.5 KB - MD5: 41d44420c6d5ea602e15e4140022af0f Code
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...
SitEnt_DE.zip Dec 10, 2019 - GER_SET: Situation Entity Type labelled corpus for German ZIP Archive - 414.5 KB - MD5: e1733e5ce7ef02577239d5a9ada0d8ba
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...
README.md Jan 23, 2020 - Encoder-Decoder Model for Semantic Role Labeling Markdown Text - 8.4 KB - MD5: 835ab4a78a83f8bea4d55dd6caa51837 Documentation
SRL-S2S.zip Jan 23, 2020 - Encoder-Decoder Model for Semantic Role Labeling ZIP Archive - 42.5 MB - MD5: 5a525ee5066a01138845a1276c110956 Code
GTTC.tab Mar 6, 2020 - German Twitter Titling Corpus Tabular Data - 119.5 KB - 5 Variables, 1904 Observations - UNF:6:hDTAU0fvrPT3em851EVmhw== Data
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.

corec-master.zip

Oct 22, 2019 - COREC – A neural multi-label COmmonsense RElation Classification system

ZIP Archive - 6.2 KB -

Code

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...

RNN_for_Clause_Classification-master.zip

Oct 22, 2019 - Genre-sensitive Neural Situation Entity classifier (DE, EN)

ZIP Archive - 12.5 KB -

Code

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...

SitEnt_DE.zip

Dec 10, 2019 - GER_SET: Situation Entity Type labelled corpus for German

ZIP Archive - 414.5 KB -

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...

README.md

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

Markdown Text - 8.4 KB -

Documentation

SRL-S2S.zip

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

ZIP Archive - 42.5 MB -

Code

GTTC.tab

Mar 6, 2020 - German Twitter Titling Corpus

Tabular Data - 119.5 KB - 5 Variables, 1904 Observations -

Data

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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