Empirical Linguistics and Computational Language Modeling (LiMo)

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161 to 170 of 184 Results

Datasets for Dependency Tree Reranking 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".
dataset_annotations.tab Oct 8, 2019 - Affixoid Dataset (DE) Tabular Data - 61.6 KB - 1 Variables, 1787 Observations - UNF:6:+MGK9lTPTXx7Rclu1BpPnw== Data
cs-ud-kbest50.tar.gz Nov 13, 2023 - Datasets for Dependency Tree Reranking Gzip Archive - 54.3 MB - MD5: e1df54b815eec985f102d48a31426107 Data
cs-ud-kbest50-top10.tar.gz Nov 13, 2023 - Datasets for Dependency Tree Reranking Gzip Archive - 21.1 MB - MD5: ceed7b0509f5d95f9d6a5003229cf770 Data
cs-rcnn-shared.tar.gz Nov 13, 2023 - Neural Rerankers for Dependency Parsing Gzip Archive - 58.0 MB - MD5: 271f2b6ad94a1b3a5b5c37db948d0b7d Data
cs-gcn.tar.gz Nov 13, 2023 - Neural Rerankers for Dependency Parsing Gzip Archive - 64.5 MB - MD5: c9066cb993f7f932b4dd3b2f57ab0df8
corec-master.zip Oct 22, 2019 - COREC – A neural multi-label COmmonsense RElation Classification system ZIP Archive - 6.2 KB - MD5: 04927f554601f39dd7e2d86a5e62d681 Code
COREC – A neural multi-label COmmonsense RElation Classification system Oct 22, 2019 Becker, Maria, 2019, "COREC – A neural multi-label COmmonsense RElation Classification system", https://doi.org/10.11588/data/E5EHBV, heiDATA, V1 We examine the learnability of Commonsense knowledge relations as represented in CONCEPTNET. We develop a neural open world multi-label classification system that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the spec...
Converter for content-to-head style syntactic dependencies Mar 26, 2020 Rehbein, Ines; Steen, Julius; Do, Bich-Ngoc; Frank, Anette, 2020, "Converter for content-to-head style syntactic dependencies", https://doi.org/10.11588/data/HE3BAZ, heiDATA, V1 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 adpositions, copula and coordination). For more information, see (Rehbein, Steen, Do & Frank 2017).
convert.zip Mar 26, 2020 - Converter for content-to-head style syntactic dependencies ZIP Archive - 10.1 MB - MD5: 30167cb475d743ced8aa63e6349a99ce CodeDocumentation

Datasets for Dependency Tree Reranking

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".

dataset_annotations.tab

Oct 8, 2019 - Affixoid Dataset (DE)

Tabular Data - 61.6 KB - 1 Variables, 1787 Observations -

Data

cs-ud-kbest50.tar.gz

Nov 13, 2023 - Datasets for Dependency Tree Reranking

Gzip Archive - 54.3 MB -

Data

cs-ud-kbest50-top10.tar.gz

Nov 13, 2023 - Datasets for Dependency Tree Reranking

Gzip Archive - 21.1 MB -

Data

cs-rcnn-shared.tar.gz

Nov 13, 2023 - Neural Rerankers for Dependency Parsing

Gzip Archive - 58.0 MB -

Data

cs-gcn.tar.gz

Nov 13, 2023 - Neural Rerankers for Dependency Parsing

Gzip Archive - 64.5 MB -

corec-master.zip

Oct 22, 2019 - COREC – A neural multi-label COmmonsense RElation Classification system

ZIP Archive - 6.2 KB -

Code

COREC – A neural multi-label COmmonsense RElation Classification system

Oct 22, 2019

Becker, Maria, 2019, "COREC – A neural multi-label COmmonsense RElation Classification system", https://doi.org/10.11588/data/E5EHBV, heiDATA, V1

We examine the learnability of Commonsense knowledge relations as represented in CONCEPTNET. We develop a neural open world multi-label classification system that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the spec...

Converter for content-to-head style syntactic dependencies

Mar 26, 2020

Rehbein, Ines; Steen, Julius; Do, Bich-Ngoc; Frank, Anette, 2020, "Converter for content-to-head style syntactic dependencies", https://doi.org/10.11588/data/HE3BAZ, heiDATA, V1

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 adpositions, copula and coordination). For more information, see (Rehbein, Steen, Do & Frank 2017).

convert.zip

Mar 26, 2020 - Converter for content-to-head style syntactic dependencies

ZIP Archive - 10.1 MB -

CodeDocumentation

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