11 to 20 of 93 Results
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... |
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines, 2023, "Tool for Extracting PP Attachment Disambiguation Dataset", https://doi.org/10.11588/data/RHD3KS, heiDATA, V1
This resource contains code to extract a PP attachment disambiguation dataset as described in the paper: Do and Rehbein (2020). "Parsers Know Best: German PP Attachment Revisited". The input is in CoNLL format, and the output format is similar to the one described in de Kok et al... |
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines, 2023, "Neural PP Attachment Disambiguation Systems", https://doi.org/10.11588/data/DKWKGJ, heiDATA, V1
This resource contains code for different types of neural PP attachment disambiguation systems: A disambiguation system inspired by de Kok et al. (2017) but with the ranking loss function. A disambiguation system with biaffine attention similar to the neural dependency parser in... |
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" |
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... |
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-... |
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... |
Oct 25, 2023 - Data Analysis and Modeling in Medicine
Marvin Kinz, 2023, "SimTool-SynBench", https://doi.org/10.11588/data/R9IKCF, heiDATA, V2
The SimTool is a toolset to simulate soft tissue deformation during resection surgery. The proposed toolset is an assimilation of 3D packages in Python and Unreal Engine 4 (UE4) with Nvidia Flex integration, which make it possible to adapt the dataset for more applications. The S... |
Aug 30, 2023 - Propylaeum@heiDATA
Mara, Hubert; Homburg, Timo, 2023, "MaiCuBeDa Hilprecht - Mainz Cuneiform Benchmark Dataset for the Hilprecht Collection", https://doi.org/10.11588/data/QSNIQ2, heiDATA, V1, UNF:6:NXlfO+rwTQYYtmBeze9QUw== [fileUNF]
Das Mainz Cuneiform Benchmark Dataset (MaiCuBeDa) beinhaltet Bilder von Keilschrifzeichen, Worten bestehend aus Keilschriftzeichen, Keilschrifzeichenzeilen und annotierten Einzelkeilen basierend auf dem Datenset HeiCuBeDa Hilprecht: https://doi.org/10.11588/data/IE8CCN . Die Anno... |
Aug 16, 2023 - arthistoricum.net@heiDATA
Knaus, Gudrun; Kailus, Angela; Stein, Regine, 2022, "LIDO-Handbuch für die Erfassung und Publikation von Metadaten zu kulturellen Objekten - Band 2: Malerei und Skulptur [Anwendungsbeispiele]", https://doi.org/10.11588/data/CHEPS6, heiDATA, V3
LIDO (Lightweight Information Describing Objects) ist ein XML-Schema für die standardkonforme Bereitstellung von Metadaten über kulturelle Objekte in einer Vielzahl von digitalen Kontexten. Basierend auf diesem internationalen Standard dient das "LIDO-Handbuch für die Erfassung u... |