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1 to 10 of 20 Results

WikiWarsDE Corpus Aug 13, 2014 - Database Systems Research Group Strötgen, Jannik; Gertz, Michael, 2014, "WikiWarsDE Corpus", https://doi.org/10.11588/data/10026, heiDATA, V1 The WikiWarsDE corpus is a German corpus containing Wikipedia articles with annotations of temporal expressions. Its creation was motivated by the English WikiWars corpus (Mazur & Dale 2010). WikiWarsDE was developed to support research on temporal information extraction and norm...
Topological Field Labeler for German Nov 13, 2023 - Neural Techniques for German Dependency Parsing Do, Bich-Ngoc; Rehbein, Ines, 2023, "Topological Field Labeler for German", https://doi.org/10.11588/data/YYNQFF, heiDATA, V1 This resource contains the code of the topological labeler used in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited". For this tool, labeling topological field is formulated as a sequence labeling task. We also include in this resource two pre-...
Text und Data Mining an wissenschaftlichen Repositorien und Publikationsservern in Deutschland - Zusammenfassung der Ergebnisse einer Umfrage im Februar und März 2016 Nov 2, 2016 - Perspektive Bibliothek Drees, Bastian, 2016, "Text und Data Mining an wissenschaftlichen Repositorien und Publikationsservern in Deutschland - Zusammenfassung der Ergebnisse einer Umfrage im Februar und März 2016", https://doi.org/10.11588/data/10090, heiDATA, V2 Es wurden die auf den Homepages angegebenen Ansprechpartner wissenschaftlicher Repositorien und Publikationsserver in Deutschland zu ihren Erfahrungen mit Text und Data Mining befragt. Die Befragung fand zwischen dem 22. und 26.2.2016 per E-Mail statt. Es wurden Ansprechpartner v...
SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code] Feb 4, 2019 - AIPHES Marasovic, Ana, 2019, "SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]", https://doi.org/10.11588/data/LWN9XE, heiDATA, V1 This repository contains code for reproducing experiments done in Marasovic and Frank (2018). Paper abstract: For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towar...
Source Code, Data and Additional Material for the Thesis: "Social Commonsense Reasoning with Structured Knowledge in Text" Jul 4, 2023 - AIPHES Paul, Debjit, 2023, "Source Code, Data and Additional Material for the Thesis: "Social Commonsense Reasoning with Structured Knowledge in Text"", https://doi.org/10.11588/data/C56QUV, heiDATA, V1 Understanding a social situation requires the ability to reason about the underlying emotions and behaviour of others. For example, when we read a personal story, we use our prior commonsense knowledge and social intelligence to infer the emotions, motives, and anticipate the act...
PatTR: Patent Translation Resource Jun 16, 2014 - Statistical Natural Language Processing Group Wäschle, Katharina; Riezler, Stefan, 2014, "PatTR: Patent Translation Resource", https://doi.org/10.11588/data/10002, heiDATA, V3 PatTR is a sentence-parallel corpus extracted from the MAREC patent collection. The current version contains more than 22 million German-English and 18 million French-English parallel sentences collected from all patent text sections as well as 5 million German-French sentence pa...
Opinion role extractor Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1 System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text
Neural Rerankers for Dependency Parsing Nov 13, 2023 - Neural Techniques for German Dependency Parsing Do, Bich-Ngoc; Rehbein, Ines, 2023, "Neural Rerankers for Dependency Parsing", https://doi.org/10.11588/data/NNGPQZ, heiDATA, V1 This resource contains code for different types of neural rerankers (RCNN, RCNN-shared and GCN) from the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation". We also include in this resource the pre-trained models of different rerankers on 3 lan...
Neural Dependency Parser with Biaffine Attention and BERT Embeddings Nov 13, 2023 - Neural Techniques for German Dependency Parsing Do, Bich-Ngoc; Rehbein, Ines, 2023, "Neural Dependency Parser with Biaffine Attention and BERT Embeddings", https://doi.org/10.11588/data/0U6IWL, heiDATA, V1 This resource contains the code of the dependency parser used in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017) and is extended to use the BERT...
Neural Dependency Parser with Biaffine Attention Nov 13, 2023 - Neural Techniques for German Dependency Parsing Fankhauser, Peter; Do, Bich-Ngoc; Kupietz, Marc, 2023, "Neural Dependency Parser with Biaffine Attention", https://doi.org/10.11588/data/DZ9MUS, heiDATA, V1 This resource contains the code of the dependency parser used in the paper: Fankhauser, et al. (2020). "Evaluating a Dependency Parser on DeReKo". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017). In addition, we include two pre-trai...

WikiWarsDE Corpus

Aug 13, 2014 - Database Systems Research Group

Strötgen, Jannik; Gertz, Michael, 2014, "WikiWarsDE Corpus", https://doi.org/10.11588/data/10026, heiDATA, V1

The WikiWarsDE corpus is a German corpus containing Wikipedia articles with annotations of temporal expressions. Its creation was motivated by the English WikiWars corpus (Mazur & Dale 2010). WikiWarsDE was developed to support research on temporal information extraction and norm...

Topological Field Labeler for German

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Do, Bich-Ngoc; Rehbein, Ines, 2023, "Topological Field Labeler for German", https://doi.org/10.11588/data/YYNQFF, heiDATA, V1

This resource contains the code of the topological labeler used in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited". For this tool, labeling topological field is formulated as a sequence labeling task. We also include in this resource two pre-...

Text und Data Mining an wissenschaftlichen Repositorien und Publikationsservern in Deutschland - Zusammenfassung der Ergebnisse einer Umfrage im Februar und März 2016

Nov 2, 2016 - Perspektive Bibliothek

Drees, Bastian, 2016, "Text und Data Mining an wissenschaftlichen Repositorien und Publikationsservern in Deutschland - Zusammenfassung der Ergebnisse einer Umfrage im Februar und März 2016", https://doi.org/10.11588/data/10090, heiDATA, V2

Es wurden die auf den Homepages angegebenen Ansprechpartner wissenschaftlicher Repositorien und Publikationsserver in Deutschland zu ihren Erfahrungen mit Text und Data Mining befragt. Die Befragung fand zwischen dem 22. und 26.2.2016 per E-Mail statt. Es wurden Ansprechpartner v...

SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]

Feb 4, 2019 - AIPHES

Marasovic, Ana, 2019, "SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]", https://doi.org/10.11588/data/LWN9XE, heiDATA, V1

This repository contains code for reproducing experiments done in Marasovic and Frank (2018). Paper abstract: For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towar...

Source Code, Data and Additional Material for the Thesis: "Social Commonsense Reasoning with Structured Knowledge in Text"

Jul 4, 2023 - AIPHES

Paul, Debjit, 2023, "Source Code, Data and Additional Material for the Thesis: "Social Commonsense Reasoning with Structured Knowledge in Text"", https://doi.org/10.11588/data/C56QUV, heiDATA, V1

Understanding a social situation requires the ability to reason about the underlying emotions and behaviour of others. For example, when we read a personal story, we use our prior commonsense knowledge and social intelligence to infer the emotions, motives, and anticipate the act...

PatTR: Patent Translation Resource

Jun 16, 2014 - Statistical Natural Language Processing Group

Wäschle, Katharina; Riezler, Stefan, 2014, "PatTR: Patent Translation Resource", https://doi.org/10.11588/data/10002, heiDATA, V3

PatTR is a sentence-parallel corpus extracted from the MAREC patent collection. The current version contains more than 22 million German-English and 18 million French-English parallel sentences collected from all patent text sections as well as 5 million German-French sentence pa...

Opinion role extractor

Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1

System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text

Neural Rerankers for Dependency Parsing

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Do, Bich-Ngoc; Rehbein, Ines, 2023, "Neural Rerankers for Dependency Parsing", https://doi.org/10.11588/data/NNGPQZ, heiDATA, V1

This resource contains code for different types of neural rerankers (RCNN, RCNN-shared and GCN) from the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation". We also include in this resource the pre-trained models of different rerankers on 3 lan...

Neural Dependency Parser with Biaffine Attention and BERT Embeddings

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Do, Bich-Ngoc; Rehbein, Ines, 2023, "Neural Dependency Parser with Biaffine Attention and BERT Embeddings", https://doi.org/10.11588/data/0U6IWL, heiDATA, V1

This resource contains the code of the dependency parser used in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017) and is extended to use the BERT...

Neural Dependency Parser with Biaffine Attention

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Fankhauser, Peter; Do, Bich-Ngoc; Kupietz, Marc, 2023, "Neural Dependency Parser with Biaffine Attention", https://doi.org/10.11588/data/DZ9MUS, heiDATA, V1

This resource contains the code of the dependency parser used in the paper: Fankhauser, et al. (2020). "Evaluating a Dependency Parser on DeReKo". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017). In addition, we include two pre-trai...

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