Below you will find an overview of experiment and annotation data developed at the IMS.
Experiment and Annotation Data of the IMS
Title | Description |
---|---|
-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 |
Association Norms | Association norms for German, English and Italian |
Automatically Generated Norms of Abstractness, Arousal, Imageability and Valence for German Lemmas | 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 |
Automatically detected non-recorded word senses in English and Swedish (NRS EN/SV) | This data collection contains English and Swedish word sense annotations from which non-recorded senses can be derived. |
Automatically generated norms for emotions & affective norms for 2.2m German Words & Analogy 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) | 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'. |
Compositionality Ratings | Compositionality ratings are human ratings on the degree of compositionality of compounds |
DWUG DE Sense | DWUG DE Sense: Historical word sense annotations in German |
Data and Implementation for State-of-the-Art Sentiment Model Evaluation | Data and Implementation for State-of-the-Art Sentiment Model Evaluation |
Database of Paradigmatic Semantic Relation Pairs | The database is a collection of semantically related word pairs in German which was compiled via human judgement experiments hosted on Amazon Mechanical Turk |
Dataset of Directional Arrows for German Particle Verbs | Dataset of Directional Arrows for German Particle Verbs |
Dataset of Human Judgements on Metaphorical Expressions in Discourse | Synonymous Metaphorical and Literal Expressions in Discourse |
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 |
Dia-Comp-NN | A collection of diachronic in-context human ratings on meaning relatedness and changes for English and German noun-noun compounds |
Diachronic Usage Relatedness (DURel) | Test Set and Annotation Data |
Domain-Specific Dataset of Difficulty Ratings for German Noun Compounds | Domains: DIY, Cooking and Automotive |
Emotion Confidence | Emotion annotations in COCA |
Emotion Stimulus Detection: Data and Implementation | Data and Implementation for English Emotion Stimulus Detection |
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions? | Data and Implementation |
Feature Norms | Feature norms are short descriptions of typical attributes for a set of objects |
Fine-grained Compound Termhood Annotation Dataset | Fine-grained Termhood Prediction for German Compound Terms using Neural Networks |
Grammaticalization of German Prepositions | Test set containing 206 German prepositions with 4 different levels of grammaticalization |
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 |
Image Generation | Experiment-Daten Bilderzeugung |
Instantiation and Hypernymy Detection | The datasets are associated with the paper 'Instances and concepts in distributional space'. |
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 and Implementation for Emotion Analysis and Transfer |
Metaphoric Change | Test Set and Annotation Data |
PAP | A Dataset for Physical and Abstract Plausibility |
Recipe Categorization – Supplementary Information | Recipe Categorization – Supplementary Information |
Resources for Modeling Derivation Using Methods from Distributional Semantics | Resources for Modeling Derivation Using Methods from Distributional Semantics |
Simulation of Lexical Semantic Change | Corpus pair and change values simulated from SemCor |
Source–Target Domains and Directionality for German Particle Verbs | Source–Target Domains and Directionality for German Particle Verbs |
Synchronic Usage Relatedness (SURel) | Test Set and Annotation Data |
Term Annotation Laypeople | A collection of judgements of laypeople, rather than experts, and specify on analysing their (dis-)agreements on common assumptions and core issues in term identification: the word classes of terms, the identification of ambiguous terms, and the relations between complex terms and possibly included subterms |
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 |
Word Usage Graphs (WUGs) | Word Usage Graphs (WUGs) represent usages of a word as nodes in a graph which are connected by weighted edges representing semantic proximity. |
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