Word & Nonword Databases

Affective Ratings for words

Description

Database of affective norms of valence, arousal, and dominance for 13,915 English words (lemmas). They are a complement of our age-of-acquisition ratings and subtitle word frequencies.

Attribution

Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

Reference

Warriner, A.B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45, 1191-1207

Wuggy: multilingual pseudoword generator

Description

Wuggy is a pseudoword generator particularly geared towards making nonwords for psycholinguistic experiments. Wuggy makes pseudowords in Basque, Dutch, English, French, German, Serbian (Cyrillic and Latin), Spanish, and Vietnamese.

Attribution

License GPL2.0

Reference

Brysbaert, M. (2010). Wuggy: A multilingual pseudoword generator. Behavior Research Methods 42(3), 627-633.

ARC Nonword Database

Description

The ARC Nonword Database, which contains 358,534 monosyllabic consisting of one syllable nonwords — 48,534 pseudohomophonesA string of letters that looks and sounds like a real word, but is not really a word included in the English dictionary and 310,000 non-pseudohomophonic nonwords. Items can be selected from the ARC Nonword Database on the basis of a wide variety of properties known or suspected to be of theoretical importance for the investigation of reading.

Attribution

Free to use

Reference

Rastle, K., Harrington, J., & Coltheart, M. (2002). 358,534 nonwords: The ARC Nonword Database. Quarterly Journal of Experimental Psychology, 55A, 1339-1362.

English Lexicon Project

Description

Data for lexical characteristics such as:

• General Characteristics such as length, frequency, and syntactic code
• Orthographic Neighborhood characteristics including number of neighbors, number of higher and lower frequency neighbors, and relative frequency
• Bigram frequency information including total bigram count, average bigram count, and sum bigram count by position
• Phonological characteristics such as pronunciation, number of phonemes, and hyphenation
• Morphological characteristics such as morphological parsing, and number of morphemes
• Part of Speech, words classified as nouns, verbs, adverbs, etc

Attribution

Word lists generated from this website are available for non-commercial research purposes only and may not be used in the development of speech technology

Reference

Balota, D.A., Yap, M.J., Cortese, M.J., Hutchison, K.A., Kessler, B., Loftis, B., Neely, J.H., Nelson, D.L., Simpson, G.B., & Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods, 39, 445-459.

Massive Auditory Lexical Decision Pseudowords

Description

Data set for speech and psycholinguistic research, 9592 pseudowords

Attribution

Attribution-NonCommercial 4.0 International

Reference

Tucker, B. V., Brenner, D., Danielson, D. K., Kelley, M. C., Nenadić, F., & Sims, M. (2019). The massive auditory lexical decision (MALD) database. Behavior research methods, 51, 1187-1204.

Massive Auditory Lexical Decision Words

Description

data set for speech and psycholinguistic research, 26,793 words

Attribution

Attribution-NonCommercial 4.0 International

Reference

Tucker, B. V., Brenner, D., Danielson, D. K., Kelley, M. C., Nenadić, F., & Sims, M. (2019). The massive auditory lexical decision (MALD) database. Behavior research methods, 51, 1187-1204.

MRC Psycholinguistic Database

Description

Database for speech and psycholinguistic research, containing 150837 words. Search based on (non exhaustive list):

• Number of letters
• Number of phonemes
• Number of syllables
• Written frequency
• Verbal Frequency
• Familiarity
• ConcretenessConcreteness represents the degree to which something can be experienced by the senses
• ImageabilityThe degree to which a word invokes a mental image
• Age of acquisition

Attribution

By citation

Reference

Wilson. M.(1988), The MRC Psycholinguistic Database: Machine Readable Dictionary, Version 2, Behavioural Research Methods, Instruments and Computers, 20, 6-11

WordNet

Description

WordNet® is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. The resulting network of meaningfully related words and concepts can be navigated with the browser. WordNet is also freely and publicly available for download. WordNet's structure makes it a useful tool for computational linguistics and natural language processing.

WordNet superficially resembles a thesaurus, in that it groups words together based on their meanings. However, there are some important distinctions. First, WordNet interlinks not just word forms—strings of letters—but specific senses of words. As a result, words that are found in close proximity to one another in the network are semantically disambiguated. Second, WordNet labels the semantic relations among words, whereas the groupings of words in a thesaurus does not follow any explicit pattern other than meaning similarity.

Attribution

By citation

Reference

Princeton University "About WordNet." WordNet. Princeton University. 2010.

The Word Finder

Description

This database offers a comprehensive range of word lists, from lists of words starting with specific letters, to lists of words of a specific length. Our database serves as an excellent reference for any researcher or anyone hoping to expand their vocabulary.

Attribution

Free to use

Reference

“Word List Database Tools- .” The Word Finder. thewordfinder.com. Retrieved Jun 5, 2023 from thewordfinder.com: https://www.thewordfinder.com/wordlist/

wordnorms.com

Single Word Norms Description

Single word norms (i.e., variables) are concept information that are only tied to a single concept, such as imaginability, concreteness, or number of phonemes. This dataset includes all cues, targets, and features from the Buchanan et al. semantic word-pair norms (2013)

Word Pairs Description

Word pair norms are cue-target groupings that require both concepts to be included to understand the relation between these concepts. For example, cat-dog can be understood in context of association (i.e., it’s raining cats and dogs) or through semantics (i.e., cats and dogs have many similar features). This dataset includes all cue-target pairings from the Buchanan et al. (2013) semantic word-pair norms

Attribution

Free to Use/ appropriate citation necessary

Reference

Buchanan, E. M., Holmes, J. L., Teasley, M. L., & Hutchison, K. A. (2013). English semantic word-pair norms and a searchable Web portal for experimental stimulus creation. Behavior research methods, 45, 746-757.