21 to 30 of 112 Results
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. |
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. |
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. |
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... |
Jul 25, 2022
Data publications of the Scientific Software Center (SSC) at Heidelberg University. |
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" |
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. |
Oct 10, 2017
Datenpublikationen des FID Altertumswissenschaften |
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... |
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... |