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Publication Year: 2020 Subject: Computer and Information Science

1 to 10 of 23 Results

tweeDe Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef; Do, Bich-Ngoc, 2020, "tweeDe", https://doi.org/10.11588/data/S90S35, heiDATA, V1 A German UD Twitter treebank, with >12,000 tokens from 519 tweets, annotated in the Universal Dependencies framework
Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review Jan 4, 2020 - Medical Informatics Benning, Nils-Hendrik; Hagen, Niclas; Knaup, Petra, 2020, "Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review", https://doi.org/10.11588/data/JRVJGN, heiDATA, V1, UNF:6:f63Fk7qwB3+Tc3vOEqFdnA== [fileUNF] This dataset contains the results (publication references) of the systematic review "Current Use of Sensor-Based Measurements for Paraplegics", presented at MIE 2020, Geneva.
Pre-trained POS tagging models for German social media Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1 Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of th...
OwnReality. To Each His Own Reality(DFK-Paris) Oct 26, 2020DFK-Paris Research data from the project OwnReality
OwnReality API-only web application Oct 26, 2020 - OwnReality. To Each His Own Reality Schepp, Moritz, 2020, "OwnReality API-only web application", https://doi.org/10.11588/data/KZHLS8, heiDATA, V1 This dataset contains the data platform for the research project "OwnReality. To Each His Own Reality". During the course of the project, data was gathered and entered into a database. In general, this platform allows the integration of that data into web based systems such as co...
Neural-Guided RANSAC for Estimating Epipolar Geometry [Data] Sep 7, 2020 - IWR Visual Learning Lab Brachmann, Eric, 2020, "Neural-Guided RANSAC for Estimating Epipolar Geometry [Data]", https://doi.org/10.11588/data/PCGYET, heiDATA, V1 Pre-computed sparse feature correspondences for pairs of images (outdoor and indoor) to reproduce the experiments described in our paper, particularly to train and evaluate NG-RANSAC. For more information, also see the code documentation: https://github.com/vislearn/ngransac
MACE-AL-TREE Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef, 2020, "MACE-AL-TREE", https://doi.org/10.11588/data/THPEBR, heiDATA, V1 An method for detecting noise in automatically annotated dependency parse trees, combining MACE (Hovy et al. 2013) with Active Learning.
MACE-AL Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef; Steen, Julius, 2020, "MACE-AL", https://doi.org/10.11588/data/C2OQN4, heiDATA, V1 A method for detecting noise in automatically annotated sequence-labelled data, combining MACE (Hovy et al. 2013) with Active Learning.
IWR Visual Learning Lab(Heidelberg University) Aug 27, 2020 This Dataverse contains visual learning related research data of the Interdisciplinary Center for Scientific Computing at Heidelberg University. Website Visual Learning Lab Heidelberg
Heidelberg Centre for Transcultural Studies (HCTS)(Heidelberg University) May 26, 2020 Open Research Data from the Heidelberg Centre for Transcultural Studies (HCTS).

tweeDe

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Ruppenhofer, Josef; Do, Bich-Ngoc, 2020, "tweeDe", https://doi.org/10.11588/data/S90S35, heiDATA, V1

A German UD Twitter treebank, with >12,000 tokens from 519 tweets, annotated in the Universal Dependencies framework

Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review

Jan 4, 2020 - Medical Informatics

Benning, Nils-Hendrik; Hagen, Niclas; Knaup, Petra, 2020, "Sensor-Based Measurements in Paraplegia: Classified References from a Systematic Review", https://doi.org/10.11588/data/JRVJGN, heiDATA, V1, UNF:6:f63Fk7qwB3+Tc3vOEqFdnA== [fileUNF]

This dataset contains the results (publication references) of the systematic review "Current Use of Sensor-Based Measurements for Paraplegics", presented at MIE 2020, Geneva.

Pre-trained POS tagging models for German social media

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1

Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of th...

OwnReality. To Each His Own Reality(DFK-Paris)

Oct 26, 2020DFK-Paris

Research data from the project OwnReality

OwnReality API-only web application

Oct 26, 2020 - OwnReality. To Each His Own Reality

Schepp, Moritz, 2020, "OwnReality API-only web application", https://doi.org/10.11588/data/KZHLS8, heiDATA, V1

This dataset contains the data platform for the research project "OwnReality. To Each His Own Reality". During the course of the project, data was gathered and entered into a database. In general, this platform allows the integration of that data into web based systems such as co...

Neural-Guided RANSAC for Estimating Epipolar Geometry [Data]

Sep 7, 2020 - IWR Visual Learning Lab

Brachmann, Eric, 2020, "Neural-Guided RANSAC for Estimating Epipolar Geometry [Data]", https://doi.org/10.11588/data/PCGYET, heiDATA, V1

Pre-computed sparse feature correspondences for pairs of images (outdoor and indoor) to reproduce the experiments described in our paper, particularly to train and evaluate NG-RANSAC. For more information, also see the code documentation: https://github.com/vislearn/ngransac

MACE-AL-TREE

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Ruppenhofer, Josef, 2020, "MACE-AL-TREE", https://doi.org/10.11588/data/THPEBR, heiDATA, V1

An method for detecting noise in automatically annotated dependency parse trees, combining MACE (Hovy et al. 2013) with Active Learning.

MACE-AL

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Ruppenhofer, Josef; Steen, Julius, 2020, "MACE-AL", https://doi.org/10.11588/data/C2OQN4, heiDATA, V1

A method for detecting noise in automatically annotated sequence-labelled data, combining MACE (Hovy et al. 2013) with Active Learning.

IWR Visual Learning Lab(Heidelberg University)

Aug 27, 2020

This Dataverse contains visual learning related research data of the Interdisciplinary Center for Scientific Computing at Heidelberg University. Website Visual Learning Lab Heidelberg

Heidelberg Centre for Transcultural Studies (HCTS)(Heidelberg University)

May 26, 2020

Open Research Data from the Heidelberg Centre for Transcultural Studies (HCTS).

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