query: A common interface for making query

In sweater: Speedy Word Embedding Association Test and Extras Using R

A common interface for making query

Description

This function makes a query based on the supplied parameters. The object can then be displayed by the S3 method print.sweater() and plotted by plot.sweater().

Usage

query(
  w,
  S_words,
  T_words,
  A_words,
  B_words,
  method = "guess",
  verbose = FALSE,
  ...
)

## S3 method for class 'sweater'
print(x, ...)

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...
T_words	a character vector of the second set of target words. In an example of studying gender stereotype, it can include occupations such as nurse, teacher, librarian...
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.
method	string, the method to be used to make the query. Available options are: weat, mac, nas, semaxis, rnsb, rnd, nas, ect and guess. If "guess", the function selects one of the following methods based on your provided wordsets. S_words & A_words - "mac" S_words, A_words & B_words - "rnd" S_words, T_words, A_words & B_words - "weat"
verbose	logical, whether to display information
...	additional parameters for the underlying function l for "semaxis": an integer indicates the number of words to augment each word in A and B based on cosine , see An et al (2018). Default to 0 (no augmentation). levels for "rnsb": levels of entries in a hierarchical dictionary that will be applied (see quanteda::dfm_lookup())
x	a sweater S3 object

Value

a sweater S3 object

See Also

weat(), mac(), nas(), semaxis(), rnsb(), rnd(), nas(), ect()

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 <- query(googlenews, S_words = S1, A_words = A1, B_words = B1)
garg_f1
plot(garg_f1)

sweater documentation built on Nov. 7, 2023, 5:08 p.m.