Empirical Linguistics and Computational Language Modeling (LiMo)

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1 to 10 of 28 Results

X-SRL Dataset and mBERT Word Aligner Feb 17, 2021 Daza, Angel, 2021, "X-SRL Dataset and mBERT Word Aligner", https://doi.org/10.11588/data/HVXXIJ, heiDATA, V1 This code contains a method to automatically align words from parallel sentences by using multilingual BERT pre-trained embeddings. This can be used to transfer source annotations (for example labeled English sentences) into the target side (for example a German translation of th...
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...
tweeDe Mar 26, 2020 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
The MSC Data Set Oct 7, 2019 Marasović, Ana; Zhou, Mengfei; Frank, Anette, 2019, "The MSC Data Set", https://doi.org/10.11588/data/JEESIQ, heiDATA, V1 From this page you can download resources we created for modal sense classification as reported in Zhou et al. (2015), Marasović et al. (2016) and Marasović and Frank (2015) (see "Related Publication" below): Heuristically sense-annotated training data acquired from EUROPARL and...
Sentiment View Lexicon (EN) Sep 5, 2019 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.
Sentiment Compound Data (DE) Sep 5, 2019 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.
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...
Opinion role extractor Sep 2, 2019 Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1 System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text
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...
Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code] Oct 7, 2019 Marasović, Ana, 2019, "Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]", https://doi.org/10.11588/data/ERDJDI, heiDATA, V1 Abstract Modal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal’s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based cl...

X-SRL Dataset and mBERT Word Aligner

Feb 17, 2021

Daza, Angel, 2021, "X-SRL Dataset and mBERT Word Aligner", https://doi.org/10.11588/data/HVXXIJ, heiDATA, V1

This code contains a method to automatically align words from parallel sentences by using multilingual BERT pre-trained embeddings. This can be used to transfer source annotations (for example labeled English sentences) into the target side (for example a German translation of th...

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...

tweeDe

Mar 26, 2020

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

The MSC Data Set

Oct 7, 2019

Marasović, Ana; Zhou, Mengfei; Frank, Anette, 2019, "The MSC Data Set", https://doi.org/10.11588/data/JEESIQ, heiDATA, V1

From this page you can download resources we created for modal sense classification as reported in Zhou et al. (2015), Marasović et al. (2016) and Marasović and Frank (2015) (see "Related Publication" below): Heuristically sense-annotated training data acquired from EUROPARL and...

Sentiment View Lexicon (EN)

Sep 5, 2019

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.

Sentiment Compound Data (DE)

Sep 5, 2019

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.

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...

Opinion role extractor

Sep 2, 2019

Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1

System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text

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...

Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]

Oct 7, 2019

Marasović, Ana, 2019, "Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]", https://doi.org/10.11588/data/ERDJDI, heiDATA, V1

Abstract Modal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal’s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based cl...

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