1 to 10 of 20 Results
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 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... |
Jul 4, 2023 - AIPHES
Paul, Debjit, 2023, "Source Code, Data and Additional Material for the Thesis: "Social Commonsense Reasoning with Structured Knowledge in Text"", https://doi.org/10.11588/data/C56QUV, heiDATA, V1
Understanding a social situation requires the ability to reason about the underlying emotions and behaviour of others. For example, when we read a personal story, we use our prior commonsense knowledge and social intelligence to infer the emotions, motives, and anticipate the act... |
Apr 18, 2023 - PhD related material - Faculty of Modern Languages
Lopez, Federico, 2023, "Source code and data for the PhD Thesis "Learning Neural Graph Representations in Non-Euclidean Geometries"", https://doi.org/10.11588/data/KOAMK4, heiDATA, V1
This dataset contains source code and data used in the PhD thesis "Learning Neural Graph Representations in Non-Euclidean Geometries". The dataset is split into four repositories: figet: Source code to run experiments for chapter 6 "Constructing and Exploiting Hierarchical Graphs... |
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... |
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, 2023Empirical Linguistics and Computational Language Modeling (LiMo)
Research Data to the PhD Projects of Ngoc Do. |
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, "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, "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... |