heiDATA

Metrics

201,384 Downloads

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

There was an error with your search parameters. Please clear your search and try again.

1 to 10 of 28 Results

Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material] Apr 24, 2024 - AIPHES Mihaylov, Todor, 2024, "Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material]", https://doi.org/10.11588/data/HU3ARF, heiDATA, V1 Machine Reading Comprehension is a language understanding task where a system is expected to read a given passage of text and typically answer questions about it. When humans assess the task of reading comprehension, in addition to the presented text, they usually use the knowled...
Head Selection Parsers and LSTM Labelers Nov 13, 2023 - Neural Techniques for German Dependency Parsing Do, Bich-Ngoc; Rehbein, Ines; Frank, Anette, 2023, "Head Selection Parsers and LSTM Labelers", https://doi.org/10.11588/data/BPWWJL, heiDATA, V1 This resource contains code, data and pre-trained models for various types of neural dependency parsers and LSTM labelers used in the papers: Do et al. (2017). "What Do We Need to Know About an Unknown Word When Parsing German" Do and Rehbein (2017). "Evaluating LSTM Models for G...
Jing bao ground truth – text block crops and annotations Nov 2, 2023 - Heidelberg Centre for Transcultural Studies (HCTS) Henke, Konstantin; Arnold, Matthias, 2023, "Jing bao ground truth – text block crops and annotations", https://doi.org/10.11588/data/PVYWKB, heiDATA, V1 This is the data set related to the paper "Language Model Assisted OCR Classification for Republican Chinese Newspaper Text", JDADH 11/2023. In this work, we present methods to obtain a neural optical character recognition (OCR) tool for article blocks in a Republican Chinese new...
CITADEL: Computational Investigation of the Topographical and Architectural Designs in an Evolving Landscape (Research Data) Jul 20, 2023 - arthistoricum.net@heiDATA Pattee, Aaron, 2023, "CITADEL: Computational Investigation of the Topographical and Architectural Designs in an Evolving Landscape (Research Data)", https://doi.org/10.11588/data/ZDOC7O, heiDATA, V1 The data found in this repository contain the basis for the historical, architectural, and geo-spatial analyses discussed in the dissertation entitled: CITADEL – Computation Investigation of the Topographical and Architectural Designs in an Evolving Landscape. These data include...
Accompanying Code and Models for Chapter 5 of the PhD Thesis "Global Inference and Local Syntax Representations for Event Extraction" Nov 24, 2021 - PhD related material - Faculty of Modern Languages Judea, Alex, 2021, "Accompanying Code and Models for Chapter 5 of the PhD Thesis "Global Inference and Local Syntax Representations for Event Extraction"", https://doi.org/10.11588/data/Z1RKOI, heiDATA, V1 This release contains the source code used for Chapter 5 of the PhD thesis "Global Inference and Local Syntax Representations for Event Extraction". It contains the implementation of the modular, graph-based event extractor described there. Furthermore, the release contains five...
Converter for content-to-head style syntactic dependencies Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Steen, Julius; Do, Bich-Ngoc; Frank, Anette, 2020, "Converter for content-to-head style syntactic dependencies", https://doi.org/10.11588/data/HE3BAZ, heiDATA, V1 A set of Python scripts that convert function-head style encodings in dependency treebanks in a content-head style encoding (as used in the UD treebanks) and vice versa (for adpositions, copula and coordination). For more information, see (Rehbein, Steen, Do & Frank 2017).
COREC – A neural multi-label COmmonsense RElation Classification system Oct 22, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) 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...
Affixoid Dataset (DE) Oct 8, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) 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...
Sentiment View Lexicon (EN) Sep 5, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) 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.
Lexicon of Abusive Words (EN) Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Wiegand, Michael, 2019, "Lexicon of Abusive Words (EN)", https://doi.org/10.11588/data/MKPEYV, heiDATA, V1 This goldstandard contains a bootstrapped lexicon of abusive words. The lexicon comprises a large set of English negative polar expressions annotated as either abusive or not.

Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material]

Apr 24, 2024 - AIPHES

Mihaylov, Todor, 2024, "Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material]", https://doi.org/10.11588/data/HU3ARF, heiDATA, V1

Machine Reading Comprehension is a language understanding task where a system is expected to read a given passage of text and typically answer questions about it. When humans assess the task of reading comprehension, in addition to the presented text, they usually use the knowled...

Head Selection Parsers and LSTM Labelers

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Do, Bich-Ngoc; Rehbein, Ines; Frank, Anette, 2023, "Head Selection Parsers and LSTM Labelers", https://doi.org/10.11588/data/BPWWJL, heiDATA, V1

This resource contains code, data and pre-trained models for various types of neural dependency parsers and LSTM labelers used in the papers: Do et al. (2017). "What Do We Need to Know About an Unknown Word When Parsing German" Do and Rehbein (2017). "Evaluating LSTM Models for G...

Jing bao ground truth – text block crops and annotations

Nov 2, 2023 - Heidelberg Centre for Transcultural Studies (HCTS)

Henke, Konstantin; Arnold, Matthias, 2023, "Jing bao ground truth – text block crops and annotations", https://doi.org/10.11588/data/PVYWKB, heiDATA, V1

This is the data set related to the paper "Language Model Assisted OCR Classification for Republican Chinese Newspaper Text", JDADH 11/2023. In this work, we present methods to obtain a neural optical character recognition (OCR) tool for article blocks in a Republican Chinese new...

CITADEL: Computational Investigation of the Topographical and Architectural Designs in an Evolving Landscape (Research Data)

Jul 20, 2023 - arthistoricum.net@heiDATA

Pattee, Aaron, 2023, "CITADEL: Computational Investigation of the Topographical and Architectural Designs in an Evolving Landscape (Research Data)", https://doi.org/10.11588/data/ZDOC7O, heiDATA, V1

The data found in this repository contain the basis for the historical, architectural, and geo-spatial analyses discussed in the dissertation entitled: CITADEL – Computation Investigation of the Topographical and Architectural Designs in an Evolving Landscape. These data include...

Accompanying Code and Models for Chapter 5 of the PhD Thesis "Global Inference and Local Syntax Representations for Event Extraction"

Nov 24, 2021 - PhD related material - Faculty of Modern Languages

Judea, Alex, 2021, "Accompanying Code and Models for Chapter 5 of the PhD Thesis "Global Inference and Local Syntax Representations for Event Extraction"", https://doi.org/10.11588/data/Z1RKOI, heiDATA, V1

This release contains the source code used for Chapter 5 of the PhD thesis "Global Inference and Local Syntax Representations for Event Extraction". It contains the implementation of the modular, graph-based event extractor described there. Furthermore, the release contains five...

Converter for content-to-head style syntactic dependencies

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Steen, Julius; Do, Bich-Ngoc; Frank, Anette, 2020, "Converter for content-to-head style syntactic dependencies", https://doi.org/10.11588/data/HE3BAZ, heiDATA, V1

A set of Python scripts that convert function-head style encodings in dependency treebanks in a content-head style encoding (as used in the UD treebanks) and vice versa (for adpositions, copula and coordination). For more information, see (Rehbein, Steen, Do & Frank 2017).

COREC – A neural multi-label COmmonsense RElation Classification system

Oct 22, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

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...

Affixoid Dataset (DE)

Oct 8, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

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...

Sentiment View Lexicon (EN)

Sep 5, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

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.

Lexicon of Abusive Words (EN)

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

Wiegand, Michael, 2019, "Lexicon of Abusive Words (EN)", https://doi.org/10.11588/data/MKPEYV, heiDATA, V1

This goldstandard contains a bootstrapped lexicon of abusive words. The lexicon comprises a large set of English negative polar expressions annotated as either abusive or not.

Add Data

Share Dataverse

Link Dataverse

Reset Modifications