Norms

Subtitle norms

• SUBTLEX-NL: frequencies based on Dutch subtitles
• SUBTLEX-US: frequencies based on American English subtitles:
• SUBTLEX-CH: frequencies based on Chinese subtitles:
• SUBTLEX-ESP: frequencies based on Spanish subtitles
• SUBTLEX-DE: frequencies based on German subtitles
• SUBTLEX-GR: frequencies based on Greek subtitles (Dimitropoulou et al., 2010)
• SUBTLEX-UK: frequencies based on British English subtitles
• SUBTLEX-PL: frequencies based on Polish subtitles
• SUBTLEX-PT: frequencies based on Portuguese subtitles
• SUBTLEX-PT-BR: frequencies based on Brazilian Portuguese subtitles

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Social media norms

• Facebook-EN: word frequencies based on public Facebook posts between November 2014 and January 2015.
• Twitter-EN: word frequencies based on the Rovereto Twitter Corpus.

Lexical norms

• Age-of-acquisition (AoA) norms for over 50,000 English words
• Age-of-acquisition (AoA) and concreteness norms for over 30,000 Dutch words
• Affective ratings for nearly 14,000 English words
• Affective ratings for over 4,000 Dutch words
• MacArthur-Bates Communicative Development Inventories
• Chinese lexical database: a new large-scale lexical database for Mandarin Chinese that provides over 150 descriptive and lexical-distributional variables for more tha n 30, 000 words in simplified Chinese.

Word association norms

• University of South Florida (USF) Free Association Norms: English word association norms for approx. 5,000 cues.
• Small World of Words: Word association norms for over 12,000 cues in English and Dutch.

Concept and category norms

• Leuven concept data: norms for over 400 concrete nouns including typicality, similarity within particular domains, category naming data, exemplar generation data, frequency, AoA, etc.
• McRae Lab feature norms: feature norm Excel files from McRae, Cree, Seidenberg, & McNorgan (2005). Data for 541 English nouns.
• Centre for Speech, Language and the Brain (CSLB) Concept Property Norms: Semantic properties and associated production frequency data for 638 concrete concepts, with data for each concept collected from 30 participants.

Semantic vectors:

• SNAUT: Interface and access to semantic vectors for Dutch and English based on word2vec
• SNAUT-Italien: Semantic spaces for Italian.
• Latent Semantic Analysis: Interface to obtain semantic similarity for words and documents
• GloVE vectors: Pretrained word vectors in English. GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

Repositories

• ESPAL: phonology, part-of-speech, subtitle frequencies, etc. in Castillian and Latin American Spanish
• Erin Buchanan’s word norms: Concept features, LSA and BEAGLE similarity estimates

Other norms

Humor: Humor norms.