41 to 50 of 60 Results
Oct 22, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
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... |
Sep 7, 2020 - IWR Visual Learning Lab
Brachmann, Eric, 2020, "Expert Sample Consensus (ESAC) for Visual Re-Localization [Data]", https://doi.org/10.11588/data/GSJE9D, heiDATA, V1
Supplementary training data for visual camera re-localization, particularly pre-computed scene coordinates to the MSR 7Scenes dataset and the Standford 12Scenes dataset. We also provide pre-trained models of our method for the 7Scenes, 12Scenes, Dubrovnik and Aachen (day) dataset... |
Jan 23, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
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... |
Sep 7, 2020 - IWR Visual Learning Lab
Brachmann, Eric, 2020, "DSAC++ Visual Camera Re-Localization [Data]", https://doi.org/10.11588/data/EGCMUU, heiDATA, V1
Supplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the Cambridge Landmarks dataset. We also provide pre-trained models of our method for the MSR 7Scenes dataset and the Cambridge Landmarks dataset. For mo... |
Aug 27, 2020 - IWR Visual Learning Lab
Brachmann, Eric, 2020, "DSAC* Visual Re-Localization [Data]", https://doi.org/10.11588/data/N07HKC, heiDATA, V1
Supplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the MSR 7Scenes dataset, and the Stanford 12Scenes dataset, as well as precomputed camera coordinate files for both aforementioned datasets. For more inf... |
Sep 7, 2020 - IWR Visual Learning Lab
Brachmann, Eric, 2020, "Differentiable RANSAC (DSAC) for Visual Re-Localization [Data]", https://doi.org/10.11588/data/3JVZSH, heiDATA, V1
Pre-trained models of our camera re-localization method for the MSR 7Scenes dataset. For more information, also see the code documentation: https://github.com/cvlab-dresden/DSAC |
Jul 15, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Nastase, Vivi; Fritz, Devon; Frank, Anette, 2019, "DeModify", https://doi.org/10.11588/data/KIWEMF, heiDATA, V1
deModify consists of 3631 instances, each with three annotations obtained through CrowdFlower. An instance is a short story in which a modifier is annotated with respect to its impact on the information in the story, assessed through its deletion from the context: crucial, not-cr... |
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... |
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). |
Jul 31, 2020 - Cluster of Excellence - Asia and Europe in a Global Context
Arnold, Matthias; Dober, Agnes, 2020, "Cataloging Cultural Objects (CCO) – The CCO Commons examples in VRA Core 4 XML", https://doi.org/10.11588/data/KKTC9G, heiDATA, V1
“Cataloging Cultural Objects - a Guide to Describing Cultural Works and Their Images” (CCO) provides a data content standard for catalogers of cultural heritage. It is a guidebook for how to populate data elements and where to apply controlled vocabulary standards. The guide is f... |
