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Author Name: Ruppenhofer, Josef

1 to 10 of 11 Results

German Twitter Titling Corpus Jan 20, 2021 - Empirical Linguistics and Computational Language Modeling (LiMo) 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...
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
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...
A harmonised testsuite for social media POS tagging (DE) Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) 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....
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.
German causal language annotations and lexicon (verbs, nouns, prepositions) (DE) Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef, 2020, "German causal language annotations and lexicon (verbs, nouns, prepositions) (DE)", https://doi.org/10.11588/data/ZHI94V, heiDATA, V1 Annotations of causal verbs, nouns and prepositions in context and lexicon file for causal verbs, nouns and prepositions.
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.
Sentiment View Lexicon (EN) Sep 5, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Wiegand, Michael; Ruppenhofer, Josef; Schulder, Marc, 2019, "Sentiment View Lexicon (EN)", https://doi.org/10.11588/data/2JK48O, heiDATA, V1 This gold standard contains sentiment expressions (verbs, nouns and adjectives) that have been annotated according to their (prior) sentiment view. Each sentiment expression is labelled either as actor or speaker view.

German Twitter Titling Corpus

Jan 20, 2021 - Empirical Linguistics and Computational Language Modeling (LiMo)

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...

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

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...

A harmonised testsuite for social media POS tagging (DE)

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

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....

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.

German causal language annotations and lexicon (verbs, nouns, prepositions) (DE)

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

Rehbein, Ines; Ruppenhofer, Josef, 2020, "German causal language annotations and lexicon (verbs, nouns, prepositions) (DE)", https://doi.org/10.11588/data/ZHI94V, heiDATA, V1

Annotations of causal verbs, nouns and prepositions in context and lexicon file for causal verbs, nouns and prepositions.

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.

Sentiment View Lexicon (EN)

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

Wiegand, Michael; Ruppenhofer, Josef; Schulder, Marc, 2019, "Sentiment View Lexicon (EN)", https://doi.org/10.11588/data/2JK48O, heiDATA, V1

This gold standard contains sentiment expressions (verbs, nouns and adjectives) that have been annotated according to their (prior) sentiment view. Each sentiment expression is labelled either as actor or speaker view.

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