Im Folgenden finden Sie eine Übersicht über die am IMS entstandenen Experiment- und Annotations-Daten.
Experiment- und Annotations-Daten des IMS
| Titel | Beschreibung |
|---|---|
| -ment derivative disambiguation data | This is the companion dataset to the Lapesa et al. 2018 article |
| Analogies in German Particle Verb Meaning Shifts | Analogies in German Particle Verb Meaning Shifts |
| Assoziationsnormen | Assoziationsnormen fürs Deutsche, Englische und Italienische |
| Automatisch generierte Normen für Abstrakheit, Arousal, Vorstellbarkeit und Valenz für deutsche Lemmata | This resource contains a collection of 350 000 German lemmatised words, rated on four psycholinguistic attributes. All ratings were obtained via a supervised learning algorithm that automatically calculates a numerical rating for each word and each affective attribute. |
| Automatisch generierte Normen für Emotionen & Affective Norms for 2.2m German Words & Analogie Dataset | Ratings for German This resource contains a collection of 2.2million German words, rated on 9 affective norms. All ratings were obtained via a supervised learning algorithm that automatically calculates a numerical rating for each word and each affective... |
| Clarifying Insertions from Revision Edits (CLAIRE) | |
| Code and Data for Hierarchical Embeddings for Hypernymy Detection and Directionality | This page contains supplementary material for the paper 'Hierarchical Embeddings for Hypernymy Detection and Directionality'. |
| DWUG DE Sense: Historische Bedeutungsannotationen im Deutschen | Historische Bedeutungsannotationen im Deutschen |
| Data and Implementation for English Emotion Stimulus Detection | Data and Implementation for English Emotion Stimulus Detection |
| Data and Implementation for State-of-the-Art Sentiment Model Evaluation | Data and Implementation for State-of-the-Art Sentiment Model Evaluation |
| Dataset of Directional Arrows for German Particle Verbs | Dataset of Directional Arrows for German Particle Verbs |
| Dataset of Literal and Non-Literal Language Usage for German Particle Verbs | Dataset of Literal and Non-Literal Language Usage for German Particle Verbs |
| Dataset of Sentence Generation for German Particle Verb Neologisms | Dataset of Sentence Generation for German Particle Verb Neologisms |
| Diachroner Wortverwendungsbezug (DURel) | Test- und Annotationsdaten |
| Domain-Specific Dataset of Difficulty Ratings for German Noun Compounds | Domains: DIY, Cooking and Automotive |
| Emotion Confidence | |
| Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions? | Data and Implementation |
| Fine-grained Compound Termhood Annotation Dataset | This is the dataset for the cited paper 'Fine-Grained Termhood Prediction for German Compound Terms Using Neural Networks'. |
| Grammatikalisierung deutscher Präpositionen | Testset mit 206 deutsche Präpositionen mit 4 unterschiedlichen Stufen der Grammatikalisierung |
| IMS at EmoInt-2017, Code and Resources | This page contains the code and resources used by our system submission for the WASSA Emotion Intensity Shared Task (EmoInt). Our system (IMS) scored 2nd out of 22. |
| Instantiation and Hypernymy Detection | The datasets are associated with the EACL 2017 paper 'Instances and concepts in distributional space'. |
| Kompositionalitätsbewertungen | Kompositätsbewertungen sind menschliche Bewertungen über den Grad der Kompositionalität von zusammengesetzten Wörtern. |
| Large-Scale Collection of English Antonym and Synonym Pairs across Word Classes | This dataset contains antonymous and synonymous pairs across adjective, noun and verb classes. These antonymous and synonymous pairs were collected from WordNet and Wordnik resources. |
| Lexical Contrast Dataset for Antonym-Synonym Distinction | Lexical Contrast Dataset for Antonym-Synonym Distinction |
| Lexical Substitution Emotion Style Transfer | Lexical Substitution Emotion Style Transfer |
| Lost in Back-Translation | Model und Implementation für Emotion Analysis und Transfer |
| Merkmalsnormen | Merkmalsnormen zu deutschen Komposita sind hier für Forschungs- und Bildungszwecke sowie den nicht-kommerziellen Einsatz kostenlos erhältlich. |
| Metaphorischer Wandel | Test Set, Annotationsdaten und Ergebnisse |
| PAP | A Dataset for Physical and Abstract Plausibility |
| Paradigmatische Semantische Relationen-Paare | Die Datenbank ist eine Sammlung von semantisch verwandten Wortpaaren auf Deutsch, die durch menschliche Bewertungsexperimente zusammengestellt wurde, die bei Amazon Mechanical Turk durchgeführt wurden. |
| Recipe Categorization – Supplementary Information | Recipe Categorization – Supplementary Information |
| Resources for Modeling Derivation Using Methods from Distributional Semantics | Resources for the paper 'Predictability of Distributional Semantics in Derivational Word Formation'. |
| Simulation von Bedeutungswandel | Korpuspaar und Bedeutungswandelwerte simuliert auf SemCor |
| Source–Target Domains and Directionality for German Particle Verbs | Source–Target Domains and Directionality for German Particle Verbs |
| Synchroner Wortverwendungsbezug (SURel) | Test Set und Annotationsdaten |
| Synonymous Metaphorical and Literal Expressions in Discourse | Synonymous Metaphorical and Literal Expressions in Discourse |
| Term Annotation Laypeople | A collection of judgements of laypeople on analysing their (dis-)agreements on common assumptions and core issues in term identification. |
| Vietnamese dataset for similarity and relatedness | This dataset consists of two kinds of datasets: The first dataset, namely ViCon, comprises pairs of synonyms and antonymys across noun, verb, and adjective classes, offerring data to distinguish between similarity and dissimilarity. The second dataset ViSim-400 is a dataset of semantic relation pairs which contains degrees of similarity across five semantic relations, as rated by human judges. |
| Wortverwendungsgraphen (WUGs) | Wortverwendungsgraphen (WUGs) repräsentieren Verwendungen eines Wortes als Knoten in einem Graphen, die durch gewichtete Kanten verbunden sind, welche semantische Nähe repräsentieren. |
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