Concreteness ratings for 40 thousand generally known English word lemmas

By Marc Brysbaert, Amy Beth Warriner, Victor Kuperman. 2014

1,837 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, Experimental and Cognitive Psychology, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

PIQA: Reasoning about Physical Commonsense in Natural Language (2019, background): “Words are colored by the average concreteness scores presented by (Brysbaert, Warriner, and Kuperman 2014). indicators of the correct answer option.”
Exploring the Limits of Weakly Supervised Pretraining (2018, methodology): “We matched the 17k Instagram hashtags with a list of 40k “concreteness” values of nouns [35] to obtain 5,730 hashtag concreteness values.”
LVIS: A Dataset for Large Vocabulary Instance Segmentation (2019, background): “proper nouns) and then finding the intersection with highly concrete common nouns [2].”
Hafez: an Interactive Poetry Generation System (2017, background): “We pre-build a word list together with a score to reﬂect its concreteness based on Brysbaert et al. (2014). f ( w ) equals to w ’s concreteness score.”
Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models (2020, methodology): “5 Additionally, we measure the average concreteness level of words in stories using the lexicon by Brysbaert et al. (2014).”
Semantic reconstruction of continuous language from non-invasive brain recordings (2022, background): “The word-level match scores were compared to behavioral ratings of valence (pleasantness), arousal (intensity of emotion), dominance (degree of exerted control) and concreteness (degree of sensory or motor experience) 69,70 .”
Computational Grounded Theory: A Methodological Framework (2020, methodology): “To test the contention that Chicago organizations used more concrete language compared to Chicago, I used a crowd-sourced database that contains a human-rated concreteness score for close to 40,000 English lemmas— the dictionary form of a word (Brysbaert, Warriner, and Kuperman 2014).”
Automatically Assessing Lexical Sophistication: Indices, Tools, Findings, and Application (2015, methodology): “Word information scores are derived from the MRC Psycholinguistic Database (Coltheart, 1981), Brysbaert et al. (2014), and Kuperman et al. (2012).”
Adversarial Language Games for Advanced Natural Language Intelligence (2019, methodology): “We adopt the concreteness annotation from Brysbaert et al. (2014). Figure 3 shows that concrete words are more vulnerable to attack, and inducing highly abstract words is challenging.”
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search (2018, methodology): “For concreteness ratings, we use the dataset of Brysbaert et al. (2013) which provides ratings for 40,000 English lemmas.”
Data Efficient Masked Language Modeling for Vision and Language (2021, methodology): “A.2 Concrete and Abstract definitions The concreteness annotation dataset (Brysbaert et al., 2014) is annotated by 20-30 annotators.”
How Concrete Language Shapes Customer Satisfaction (2020, methodology): “Each of these examples increase in linguistic concreteness (MRC psycholinguistic database; Brysbaert et al. 2014; Coltheart 1981; Paetzhold and Specia 2016; Spreen and Schulz 1966)2 because they describe things or actions in a more vivid, perceptible, tangible, detailed, or specific manner.”
Action Modifiers: Learning From Adverbs in Instructional Videos (2019, methodology): “We explored automatic approaches such as word concreteness scores [5], but found these approaches to be unreliable.”