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Subject: Computer and Information Science

21 to 30 of 112 Results

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.
Sentiment Compound Data (DE) Sep 5, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) 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.
Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review Jan 4, 2020 - Medical Informatics Benning, Nils-Hendrik; Hagen, Niclas; Knaup, Petra, 2020, "Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review", https://doi.org/10.11588/data/JRVJGN, heiDATA, V1, UNF:6:f63Fk7qwB3+Tc3vOEqFdnA== [fileUNF] This dataset contains the results (publication references) of the systematic review "Current Use of Sensor-Based Measurements for Paraplegics", presented at MIE 2020, Geneva.
Selectional Preference Embeddings (EMNLP 2017) Jan 31, 2019 - AIPHES Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1 Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the...
Scientific Software Center (SSC)(Scientific Software Center (SSC)) Jul 25, 2022 Data publications of the Scientific Software Center (SSC) at Heidelberg University.
Real-World PP Attachment Disambiguation Dataset Nov 13, 2023 - Neural Techniques for German Dependency Parsing Do, Bich-Ngoc; Rehbein, Ines, 2023, "Real-World PP Attachment Disambiguation Dataset", https://doi.org/10.11588/data/NB46XR, heiDATA, V1 This resource contains a German dataset for real-world PP attachment disambiguation. The creation, analysis and experiment results of the dataset are described in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited"
RATIO_EXPLAIN(Heidelberg University, Department of Computational Linguistics) Feb 26, 2024 Open Research Data from the ExpLAIN project, a joint research project of the NLP Group at the Computational Linguistics Department of Heidelberg University and the Data and Web Science Groupat University of Mannheim.
Propylaeum@heiDATA( Fachinformationsdienst Altertumswissenschaften) Oct 10, 2017 Datenpublikationen des FID Altertumswissenschaften
Prediction and Comparative Analysis of CTCF Binding Sites based on a First Principle Approach [Research Data] Mar 7, 2022 - Theoretical Physics Li, Kunhe; Oiwa, Nestor Norio; Cordeiro, Claudette E.; Heermann, Dieter W., 2022, "Prediction and Comparative Analysis of CTCF Binding Sites based on a First Principle Approach [Research Data]", https://doi.org/10.11588/data/RDISCE, heiDATA, V1 The file contains the CTCF-DNA binding sites for complete genome of Homo sapiens (human), Mus musculus (mouse), Sus scrofa (pig), Capra hircus (goat), Aedes aegypti (dengue and yellow fever mosquito) and Drosophila melanogaster (fruit fly) using electronic nucleotide alignment. T...
Pre-trained POS tagging models for German social media Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1 Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of th...

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.

Sentiment Compound Data (DE)

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

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.

Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review

Jan 4, 2020 - Medical Informatics

Benning, Nils-Hendrik; Hagen, Niclas; Knaup, Petra, 2020, "Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review", https://doi.org/10.11588/data/JRVJGN, heiDATA, V1, UNF:6:f63Fk7qwB3+Tc3vOEqFdnA== [fileUNF]

This dataset contains the results (publication references) of the systematic review "Current Use of Sensor-Based Measurements for Paraplegics", presented at MIE 2020, Geneva.

Selectional Preference Embeddings (EMNLP 2017)

Jan 31, 2019 - AIPHES

Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1

Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the...

Scientific Software Center (SSC)(Scientific Software Center (SSC))

Jul 25, 2022

Data publications of the Scientific Software Center (SSC) at Heidelberg University.

Real-World PP Attachment Disambiguation Dataset

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Do, Bich-Ngoc; Rehbein, Ines, 2023, "Real-World PP Attachment Disambiguation Dataset", https://doi.org/10.11588/data/NB46XR, heiDATA, V1

This resource contains a German dataset for real-world PP attachment disambiguation. The creation, analysis and experiment results of the dataset are described in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited"

RATIO_EXPLAIN(Heidelberg University, Department of Computational Linguistics)

Feb 26, 2024

Open Research Data from the ExpLAIN project, a joint research project of the NLP Group at the Computational Linguistics Department of Heidelberg University and the Data and Web Science Groupat University of Mannheim.

Propylaeum@heiDATA( Fachinformationsdienst Altertumswissenschaften)

Oct 10, 2017

Datenpublikationen des FID Altertumswissenschaften

Prediction and Comparative Analysis of CTCF Binding Sites based on a First Principle Approach [Research Data]

Mar 7, 2022 - Theoretical Physics

Li, Kunhe; Oiwa, Nestor Norio; Cordeiro, Claudette E.; Heermann, Dieter W., 2022, "Prediction and Comparative Analysis of CTCF Binding Sites based on a First Principle Approach [Research Data]", https://doi.org/10.11588/data/RDISCE, heiDATA, V1

The file contains the CTCF-DNA binding sites for complete genome of Homo sapiens (human), Mus musculus (mouse), Sus scrofa (pig), Capra hircus (goat), Aedes aegypti (dengue and yellow fever mosquito) and Drosophila melanogaster (fruit fly) using electronic nucleotide alignment. T...

Pre-trained POS tagging models for German social media

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

Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1

Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of th...

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