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Tool for Extracting PP Attachment Disambiguation Dataset
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... -
Neural Rerankers for Dependency Parsing
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:... -
Real-World PP Attachment Disambiguation Dataset
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... -
Converter for content-to-head style syntactic dependencies
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... -
Datasets for Dependency Tree Reranking
This resource contains the datasets for dependency tree reranking in 3 languages: English, German and Czech. The creation, analysis and experiment results of the datasets are... -
Neural PP Attachment Disambiguation Systems
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... -
Head Selection Parsers and LSTM Labelers
This resource contains code, data and pre-trained models for various types of neural dependency parsers and LSTM labelers used in the papers: Do et al. (2017). "What Do We Need... -
Neural Dependency Parser with Biaffine Attention and BERT Embeddings
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... -
Neural Dependency Parser with Biaffine Attention and BERT Embeddings
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... -
Head Selection Parsers and LSTM Labelers
This resource contains code, data and pre-trained models for various types of neural dependency parsers and LSTM labelers used in the papers: Do et al. (2017). "What Do We Need... -
Neural PP Attachment Disambiguation Systems
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... -
Datasets for Dependency Tree Reranking
This resource contains the datasets for dependency tree reranking in 3 languages: English, German and Czech. The creation, analysis and experiment results of the datasets are... -
Converter for content-to-head style syntactic dependencies
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... -
Real-World PP Attachment Disambiguation Dataset
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... -
Neural Rerankers for Dependency Parsing
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:... -
Tool for Extracting PP Attachment Disambiguation Dataset
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...
