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Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Fankhauser, Peter; Do, Bich-Ngoc; Kupietz, Marc, 2023, "Neural Dependency Parser with Biaffine Attention", https://doi.org/10.11588/data/DZ9MUS, heiDATA, V1
This resource contains the code of the dependency parser used in the paper: Fankhauser, et al. (2020). "Evaluating a Dependency Parser on DeReKo". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017). In addition, we include two pre-trai...
Markdown Text - 1.8 KB - MD5: 5b149f86795cd84547379060d779cc11
Documentation
Markdown Text - 232 B - MD5: a8b24a0d64ab866f78a8b474344acfcf
Documentation
Gzip Archive - 361.9 MB - MD5: a40f342adfcff8bf8217d6c7da8c4082
Data
Gzip Archive - 361.8 MB - MD5: c31097376ca9d2b08c013fcdbba10c6d
Data
ZIP Archive - 36.0 KB - MD5: 3e8f69e918c003c92700af524474ad31
Code
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines, 2023, "Datasets for Dependency Tree Reranking", https://doi.org/10.11588/data/E5NOYH, heiDATA, V1
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 described in the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation".
Gzip Archive - 54.3 MB - MD5: e1df54b815eec985f102d48a31426107
Data
Gzip Archive - 21.1 MB - MD5: ceed7b0509f5d95f9d6a5003229cf770
Data
Gzip Archive - 32.4 MB - MD5: bcaf176e4f2a5b396441afb78ae698d7
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