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Deposit Date: 2019 Subject: Computer and Information Science

1 to 10 of 13 Results

HeiCuBeDa Hilprecht - Heidelberg Cuneiform Benchmark Dataset for the Hilprecht Collection Mar 26, 2021 - IWR Computer Graphics Mara, Hubert, 2019, "HeiCuBeDa Hilprecht - Heidelberg Cuneiform Benchmark Dataset for the Hilprecht Collection", https://doi.org/10.11588/data/IE8CCN, heiDATA, V2 The number of known cuneiform tablets is assumed to be in the hundreds of thousands. A fraction has been published by printing photographs and manual tracings in books, which is collected by the online Cuneiform Digital Library Initiative (CDLI) catalog including some of these im...
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...
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.
3D Micro-Mapping of Subsidence Stations [Source Code and Data] Jan 15, 2020 - 3D Spatial Data Processing Herfort, Benjamin; Anders, Katharina; Marx, Sabrina; Eberlein, Stefan; Höfle, Bernhard, 2020, "3D Micro-Mapping of Subsidence Stations [Source Code and Data]", https://doi.org/10.11588/data/OU8YA1, heiDATA, V1 This dataset comprises the source code to reproduce the 3D micro-mapping tool for plane adjustment at subsidence stations. In this project, users adjust a plane (height and orientation) at the positions of fixed poles, so-called subsidence stations, to provide information on the...
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.
GER_SET: Situation Entity Type labelled corpus for German Dec 10, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Becker, Maria, 2019, "GER_SET: Situation Entity Type labelled corpus for German", https://doi.org/10.11588/data/BBQYD0, heiDATA, V1 Semantic clause types, also called Situation Entity (SE) types (Smith, 2003) are linguistic characterizations of aspectual properties shown to be useful for tasks like argumentation structure analysis (Becker et al., 2016), genre characterization (Palmer and Friedrich, 2014), and...
COREC – A neural multi-label COmmonsense RElation Classification system Oct 22, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) 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...
Affixoid Dataset (DE) Oct 8, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Ruppenhofer, Josef, 2019, "Affixoid Dataset (DE)", https://doi.org/10.11588/data/QKF4LT, heiDATA, V1, UNF:6:+MGK9lTPTXx7Rclu1BpPnw== [fileUNF] The dataset contains the manual annotations for the COLING 2018 submission "Distinguishing affixoid formations from compounds" by Josef Ruppenhofer, Michael Wiegand, Rebecca Wilm and Katja Markert. 1788 complex words containing one of 7 German suffixoid candidates (e.g. -hai, -go...
Sentiment Compound Data (DE) Sep 5, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Wiegand, Michael; Bocionek, Christine; Ruppenhofer, Josef, 2019, "Sentiment Compound Data (DE)", https://doi.org/10.11588/data/LSTRK3, heiDATA, V1 This dataset contains gold standards that are required for building a classifier that automatically extracts opinion (noun) compounds.

HeiCuBeDa Hilprecht - Heidelberg Cuneiform Benchmark Dataset for the Hilprecht Collection

Mar 26, 2021 - IWR Computer Graphics

Mara, Hubert, 2019, "HeiCuBeDa Hilprecht - Heidelberg Cuneiform Benchmark Dataset for the Hilprecht Collection", https://doi.org/10.11588/data/IE8CCN, heiDATA, V2

The number of known cuneiform tablets is assumed to be in the hundreds of thousands. A fraction has been published by printing photographs and manual tracings in books, which is collected by the online Cuneiform Digital Library Initiative (CDLI) catalog including some of these im...

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...

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.

3D Micro-Mapping of Subsidence Stations [Source Code and Data]

Jan 15, 2020 - 3D Spatial Data Processing

Herfort, Benjamin; Anders, Katharina; Marx, Sabrina; Eberlein, Stefan; Höfle, Bernhard, 2020, "3D Micro-Mapping of Subsidence Stations [Source Code and Data]", https://doi.org/10.11588/data/OU8YA1, heiDATA, V1

This dataset comprises the source code to reproduce the 3D micro-mapping tool for plane adjustment at subsidence stations. In this project, users adjust a plane (height and orientation) at the positions of fixed poles, so-called subsidence stations, to provide information on the...

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.

GER_SET: Situation Entity Type labelled corpus for German

Dec 10, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

Becker, Maria, 2019, "GER_SET: Situation Entity Type labelled corpus for German", https://doi.org/10.11588/data/BBQYD0, heiDATA, V1

Semantic clause types, also called Situation Entity (SE) types (Smith, 2003) are linguistic characterizations of aspectual properties shown to be useful for tasks like argumentation structure analysis (Becker et al., 2016), genre characterization (Palmer and Friedrich, 2014), and...

COREC – A neural multi-label COmmonsense RElation Classification system

Oct 22, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

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...

Affixoid Dataset (DE)

Oct 8, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

Ruppenhofer, Josef, 2019, "Affixoid Dataset (DE)", https://doi.org/10.11588/data/QKF4LT, heiDATA, V1, UNF:6:+MGK9lTPTXx7Rclu1BpPnw== [fileUNF]

The dataset contains the manual annotations for the COLING 2018 submission "Distinguishing affixoid formations from compounds" by Josef Ruppenhofer, Michael Wiegand, Rebecca Wilm and Katja Markert. 1788 complex words containing one of 7 German suffixoid candidates (e.g. -hai, -go...

Sentiment Compound Data (DE)

Sep 5, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

Wiegand, Michael; Bocionek, Christine; Ruppenhofer, Josef, 2019, "Sentiment Compound Data (DE)", https://doi.org/10.11588/data/LSTRK3, heiDATA, V1

This dataset contains gold standards that are required for building a classifier that automatically extracts opinion (noun) compounds.

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