sweater (version 0.1.8)

rnd: Relative Norm Distance

Description

This function calculate the relative norm distance (RND) of word embeddings. If possible, please use query() instead.

Usage

rnd(w, S_words, A_words, B_words, verbose = FALSE)

Value

A list with class "rnd" containing the following components:

• $norm_diff a vector of relative norm distances for every word in S_words

• $S_words the input S_words

• $A_words the input A_words

• $B_words the input B_words rnd_es() can be used to obtain the effect size of the test.

Arguments

w

a numeric matrix of word embeddings, e.g. from read_word2vec()

S_words

a character vector of the first set of target words. In an example of studying gender stereotype, it can include occupations such as programmer, engineer, scientists...

A_words

a character vector of the first set of attribute words. In an example of studying gender stereotype, it can include words such as man, male, he, his.

B_words

a character vector of the second set of attribute words. In an example of studying gender stereotype, it can include words such as woman, female, she, her.

verbose

logical, whether to display information

References

Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018). Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16), E3635-E3644. tools:::Rd_expr_doi("10.1073/pnas.1720347115")

Examples

data(googlenews)
S1 <- c("janitor", "statistician", "midwife", "bailiff", "auctioneer",
"photographer", "geologist", "shoemaker", "athlete", "cashier", "dancer",
"housekeeper", "accountant", "physicist", "gardener", "dentist", "weaver",
"blacksmith", "psychologist", "supervisor", "mathematician", "surveyor",
"tailor", "designer", "economist", "mechanic", "laborer", "postmaster",
"broker", "chemist", "librarian", "attendant", "clerical", "musician",
"porter", "scientist", "carpenter", "sailor", "instructor", "sheriff",
"pilot", "inspector", "mason", "baker", "administrator", "architect",
"collector", "operator", "surgeon", "driver", "painter", "conductor",
"nurse", "cook", "engineer", "retired", "sales", "lawyer", "clergy",
"physician", "farmer", "clerk", "manager", "guard", "artist", "smith",
"official", "police", "doctor", "professor", "student", "judge",
"teacher", "author", "secretary", "soldier")
A1 <- c("he", "son", "his", "him", "father", "man", "boy", "himself",
"male", "brother", "sons", "fathers", "men", "boys", "males", "brothers",
"uncle", "uncles", "nephew", "nephews")
B1 <- c("she", "daughter", "hers", "her", "mother", "woman", "girl",
"herself", "female", "sister", "daughters", "mothers", "women", "girls",
"females", "sisters", "aunt", "aunts", "niece", "nieces")
garg_f1 <- rnd(googlenews, S1, A1, B1)
plot_bias(garg_f1)