41 to 50 of 184 Results
Nov 13, 2023 -
Neural Rerankers for Dependency Parsing
Gzip Archive - 93.1 MB -
MD5: f2ed8e0973b3953701d0193e597737b4
|
Nov 13, 2023 -
Neural Rerankers for Dependency Parsing
Gzip Archive - 23.8 MB -
MD5: a87f5b576b0c021a0801ebc8e6c83d7c
|
Nov 13, 2023 -
Neural Rerankers for Dependency Parsing
Gzip Archive - 17.8 MB -
MD5: 0697f8c0662f6548b6dd06b1680a09df
|
Nov 13, 2023 -
Neural Rerankers for Dependency Parsing
Gzip Archive - 113.7 MB -
MD5: 81c3265a0c8b9fbe73f790d353289a77
|
Nov 13, 2023 -
Neural Rerankers for Dependency Parsing
ZIP Archive - 91.3 KB -
MD5: 9940971e18bcec152cca698bbf71c0e8
|
Nov 13, 2023 -
Neural Rerankers for Dependency Parsing
Markdown Text - 1.5 KB -
MD5: 9b99b461dc66b9e2d63bd143ffa13919
|
Nov 13, 2023 -
Neural Rerankers for Dependency Parsing
Markdown Text - 82 B -
MD5: 6f6c9146cb4bb5767db27672dcc6103f
|
Nov 13, 2023 -
Neural Rerankers for Dependency Parsing
Markdown Text - 577 B -
MD5: cfbb3eb52b0506ccc177b204a8be2577
|
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 -
Tool for Extracting PP Attachment Disambiguation Dataset
ZIP Archive - 11.6 KB -
MD5: 9fb938cbabb83b7434f5e790be72c80f
|