Empirical Linguistics and Computational Language Modeling (LiMo)

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Publication Year: 2020

11 to 20 of 27 Results

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...
output.txt Mar 26, 2020 - Pre-trained POS tagging models for German social media Plain Text - 639 B - MD5: 1dc8fa318270ae3612341c0065889549
MACE-AL.zip Mar 26, 2020 - MACE-AL ZIP Archive - 326.8 KB - MD5: 056a7e70a8f8b6e8fa72e3eead763d39 Code
MACE-AL-TREE.zip Mar 26, 2020 - MACE-AL-TREE ZIP Archive - 141.9 KB - MD5: 7de327971177c2124d8f388a19b1c4c6 Code
MACE-AL-TREE Mar 26, 2020 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 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.
hunpos-social-media.model.bz2 Mar 26, 2020 - Pre-trained POS tagging models for German social media Bzip Archive - 6.0 MB - MD5: 130e09643a6ec5b26bcdf520571f261d Data
GTTC_addendum.tab Jan 20, 2021 - German Twitter Titling Corpus Tabular Data - 19.7 KB - 5 Variables, 296 Observations - UNF:6:e8JLFj0rmt8hCbrLS38QTg== Data
GTTC.tab Mar 6, 2020 - German Twitter Titling Corpus Tabular Data - 119.5 KB - 5 Variables, 1904 Observations - UNF:6:hDTAU0fvrPT3em851EVmhw== Data
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...

output.txt

Mar 26, 2020 - Pre-trained POS tagging models for German social media

Plain Text - 639 B -

MACE-AL.zip

Mar 26, 2020 - MACE-AL

ZIP Archive - 326.8 KB -

Code

MACE-AL-TREE.zip

Mar 26, 2020 - MACE-AL-TREE

ZIP Archive - 141.9 KB -

Code

MACE-AL-TREE

Mar 26, 2020

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

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.

hunpos-social-media.model.bz2

Mar 26, 2020 - Pre-trained POS tagging models for German social media

Bzip Archive - 6.0 MB -

Data

GTTC_addendum.tab

Jan 20, 2021 - German Twitter Titling Corpus

Tabular Data - 19.7 KB - 5 Variables, 296 Observations -

Data

GTTC.tab

Mar 6, 2020 - German Twitter Titling Corpus

Tabular Data - 119.5 KB - 5 Variables, 1904 Observations -

Data

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...

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