Affective Norms: Concreteness vs. Abstractness, Arousal, Imageability, Valence
Over the years, we collected and generated a range of affective norms, focusing on concreteness (vs. abstractness) norms, but also including other affective norms.- Abstract words refer to things that we cannot perceive directly with our senses (idea, politics, ...), concrete words refer to things that we can perceive (image, scent, ...).
- A large subset of concrete words has a high imageability, i.e., these words refer to things that we can actually see.
- Valence determines the pleasantness of a word (gift vs. punishment).
- Arousal describes the intensity of emotion provoked by a stimulus (alert vs. calm).
Multilingual Concreteness Event Norms
In our projects MUDCAT and MeTRapher, we created the following
concreteness norms for verb-object and subject-verb-object events
across languages.
VO-MultiLingConcMet
A dataset containing 5,814/430,000 collected/LLM-extrapolated multilingual concreteness ratings for verb-object events in English, German, and Slovene, plus figurative language judgements (incl. example sentences)
VO-SVO-EnglishConcMet
A dataset containing approx. 4,000/500 English verb-object/subject-verb-object events with concreteness ratings and figurative language judgements (incl. example sentences)
Affective Ratings for German
This resource contains a collection of 350,000 German lemmatised words, rated on the four affective attributes. All ratings were obtained via a supervised learning algorithm that automatically calculates a numerical rating for each word and each affective attribute.
See here
on how to obtain the data.
Abstractness/Concreteness Ratings for Estonian
This resource contains a collection of 243,675 Estonian lemmatised words, rated on abstractness. These ratings were obtained via the same supervised learning algorithm as the German ratings, but based on translation seeds from English.See here
on how to obtain the data.
Abstractness/Concreteness Ratings for Modern Standard Arabic and Romanian
We collected human concreteness ratings for 202 Modern Standard Arabic and 300 Romanian nouns. The resources are available on request.Abstractness/Concreteness Ratings for English
We combined a feed-forward neural network, existing ratings for English and word2vec cbow word vectors trained on a Google-internal news corpus with 100 billion tokens to propagate abstractness ratings to a vocabulary of 3 million words.See here
on how to obtain the data.
References:
Eleri Aedmaa, Maximilian Köper, Sabine Schulte im Walde (2018)
Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs
In: Proceedings of the NAACL 2018 Student Research Workshop (NAACL-SRW). New Orleans, LA.
Urban Knupleš, Diego Frassinelli, Alexander Fraser, Sabine Schulte im Walde
Literally Concrete or Figuratively Abstract? Multilingual Concreteness Norms for Verb-Object Expressions [resource]
Transactions of the Association for Computational Linguistics (TACL). 2026.
Maximilian Köper, Sabine Schulte im Walde (2016)
Automatically Generated Norms of Abstractness, Arousal, Imageability and Valence for 350 000 German Lemmas
In: Proceedings of the 10th Conference on Language Resources and Evaluation (LREC). Portoroz, Slovenia.
Maximilian Köper, Sabine Schulte im Walde (2017)
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses
In: Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications (SENSE). Valencia, Spain.