Empirical Linguistics and Computational Language Modeling (LiMo)

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1 to 7 of 7 Results

German Twitter Titling Corpus Jan 20, 2021 van den Berg, Esther; Korfhage, Katharina; Ruppenhofer, Josef; Wiegand, Michael; Markert, Katja, 2020, "German Twitter Titling Corpus", https://doi.org/10.11588/data/AOSUY6, heiDATA, V2, UNF:6:14BxjwJS7Q3mfI6ei7iBBw== [fileUNF] The German Titling Twitter Corpus consists of 1904 stance-annotated tweets collected in June/July 2018 mentioning 24 German politicians with a doctoral degree. The Addendum contains an additional 296 stance-annotated tweets from each month of 2018 mentioning 10 politicians with a...
Pre-trained POS tagging models for German social media Mar 26, 2020 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...
A harmonised testsuite for social media POS tagging (DE) Mar 26, 2020 Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "A harmonised testsuite for social media POS tagging (DE)", https://doi.org/10.11588/data/KXLMHN, heiDATA, V1 A harmonised POS testsuite of web data, CMC and Twitter microtext, with word forms and STTS pos tags (+ some additional CMC-specific tags). UD pos tags have been automatically converted, based on the STTS pos tags. The data does not contain (manually corrected) lemma information....
GermEval-2018 Corpus (DE) Sep 2, 2019 Wiegand, Michael, 2019, "GermEval-2018 Corpus (DE)", https://doi.org/10.11588/data/0B5VML, heiDATA, V1 This dataset comprises the training and test data (German tweets) from the GermEval 2018 Shared on Offensive Language Detection.
Twitter Titling Corpus Aug 23, 2019 van den Berg, Esther; Korfhage, Katharina; Ruppenhofer, Josef; Wiegand, Michael; Markert, Katja, 2019, "Twitter Titling Corpus", https://doi.org/10.11588/data/IOHXDF, heiDATA, V1, UNF:6:+F3lLKziwMvjy+xyktkilw== [fileUNF] The Twitter Titling Corpus contains 4002 stance-annotated tweets collected between 20 June 2017 and 30 August 2017 mentioning 6 presidents. Each tweet is annotated for the naming form used to refer to the president, for the purpose of a study on the relation between naming variat...
Negative Sampling for Learning Knowledge Graph Embeddings Aug 19, 2019 Kotnis, Bhushan, 2019, "Negative Sampling for Learning Knowledge Graph Embeddings", https://doi.org/10.11588/data/YYULL2, heiDATA, V1 Reimplementation of four KG factorization methods and six negative sampling methods. Abstract Knowledge graphs are large, useful, but incomplete knowledge repositories. They encode knowledge through entities and relations which define each other through the connective structure o...
KGE Algorithms Aug 19, 2019 Kotnis, Bhushan, 2019, "KGE Algorithms", https://doi.org/10.11588/data/CSXYSS, heiDATA, V1 An updated method for link prediction that uses a regularization factor that models relation argument types Abstract (Kotnis and Nastase, 2017): Learning relations based on evidence from knowledge repositories relies on processing the available relation instances. Knowledge repos...

German Twitter Titling Corpus

Jan 20, 2021

van den Berg, Esther; Korfhage, Katharina; Ruppenhofer, Josef; Wiegand, Michael; Markert, Katja, 2020, "German Twitter Titling Corpus", https://doi.org/10.11588/data/AOSUY6, heiDATA, V2, UNF:6:14BxjwJS7Q3mfI6ei7iBBw== [fileUNF]

The German Titling Twitter Corpus consists of 1904 stance-annotated tweets collected in June/July 2018 mentioning 24 German politicians with a doctoral degree. The Addendum contains an additional 296 stance-annotated tweets from each month of 2018 mentioning 10 politicians with a...

Pre-trained POS tagging models for German social media

Mar 26, 2020

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...

A harmonised testsuite for social media POS tagging (DE)

Mar 26, 2020

Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "A harmonised testsuite for social media POS tagging (DE)", https://doi.org/10.11588/data/KXLMHN, heiDATA, V1

A harmonised POS testsuite of web data, CMC and Twitter microtext, with word forms and STTS pos tags (+ some additional CMC-specific tags). UD pos tags have been automatically converted, based on the STTS pos tags. The data does not contain (manually corrected) lemma information....

GermEval-2018 Corpus (DE)

Sep 2, 2019

Wiegand, Michael, 2019, "GermEval-2018 Corpus (DE)", https://doi.org/10.11588/data/0B5VML, heiDATA, V1

This dataset comprises the training and test data (German tweets) from the GermEval 2018 Shared on Offensive Language Detection.

Twitter Titling Corpus

Aug 23, 2019

van den Berg, Esther; Korfhage, Katharina; Ruppenhofer, Josef; Wiegand, Michael; Markert, Katja, 2019, "Twitter Titling Corpus", https://doi.org/10.11588/data/IOHXDF, heiDATA, V1, UNF:6:+F3lLKziwMvjy+xyktkilw== [fileUNF]

The Twitter Titling Corpus contains 4002 stance-annotated tweets collected between 20 June 2017 and 30 August 2017 mentioning 6 presidents. Each tweet is annotated for the naming form used to refer to the president, for the purpose of a study on the relation between naming variat...

Negative Sampling for Learning Knowledge Graph Embeddings

Aug 19, 2019

Kotnis, Bhushan, 2019, "Negative Sampling for Learning Knowledge Graph Embeddings", https://doi.org/10.11588/data/YYULL2, heiDATA, V1

Reimplementation of four KG factorization methods and six negative sampling methods. Abstract Knowledge graphs are large, useful, but incomplete knowledge repositories. They encode knowledge through entities and relations which define each other through the connective structure o...

KGE Algorithms

Aug 19, 2019

Kotnis, Bhushan, 2019, "KGE Algorithms", https://doi.org/10.11588/data/CSXYSS, heiDATA, V1

An updated method for link prediction that uses a regularization factor that models relation argument types Abstract (Kotnis and Nastase, 2017): Learning relations based on evidence from knowledge repositories relies on processing the available relation instances. Knowledge repos...

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