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Publication Year: 2019 Subject: Computer and Information Science

1 to 10 of 28 Results

Twitter Titling Corpus Aug 23, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) 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...
The MSC Data Set Oct 7, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) 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...
SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code] Feb 4, 2019 - AIPHES Marasovic, Ana, 2019, "SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]", https://doi.org/10.11588/data/LWN9XE, heiDATA, V1 This repository contains code for reproducing experiments done in Marasovic and Frank (2018). Paper abstract: For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towar...
Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis" Feb 6, 2019 - AIPHES Heinzerling, Benjamin, 2019, "Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis"", https://doi.org/10.11588/data/9JKAVW, heiDATA, V1 This dataset contains source code and system output used in the PhD thesis "Aspects of Coherence for Entity Analysis". This dataset is split into three parts corresponding to the chapters describing the three main contributions of the thesis: chapter3.tar.gz: Java source code for...
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.
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.
Selectional Preference Embeddings (EMNLP 2017) Jan 31, 2019 - AIPHES Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1 Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the...
Opinion role extractor Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) 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 - Empirical Linguistics and Computational Language Modeling (LiMo) 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 - Empirical Linguistics and Computational Language Modeling (LiMo) 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...

Twitter Titling Corpus

Aug 23, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

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...

The MSC Data Set

Oct 7, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

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...

SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]

Feb 4, 2019 - AIPHES

Marasovic, Ana, 2019, "SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]", https://doi.org/10.11588/data/LWN9XE, heiDATA, V1

This repository contains code for reproducing experiments done in Marasovic and Frank (2018). Paper abstract: For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towar...

Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis"

Feb 6, 2019 - AIPHES

Heinzerling, Benjamin, 2019, "Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis"", https://doi.org/10.11588/data/9JKAVW, heiDATA, V1

This dataset contains source code and system output used in the PhD thesis "Aspects of Coherence for Entity Analysis". This dataset is split into three parts corresponding to the chapters describing the three main contributions of the thesis: chapter3.tar.gz: Java source code for...

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.

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.

Selectional Preference Embeddings (EMNLP 2017)

Jan 31, 2019 - AIPHES

Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1

Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the...

Opinion role extractor

Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

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 - Empirical Linguistics and Computational Language Modeling (LiMo)

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 - Empirical Linguistics and Computational Language Modeling (LiMo)

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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