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Extract word vector(s).

Source: R/01-basic.R
get_wordvec.Rd

Extract word vector(s), using either a list of words or a regular expression.

Usage

get_wordvec(
  data,
  words = NULL,
  pattern = NULL,
  plot = FALSE,
  plot.dims = NULL,
  plot.step = 0.05,
  plot.border = "white"
)

Arguments

data

A wordvec (data.table) or embed (matrix), see data_wordvec_load.

words

[Option 1] Character string(s).

pattern

[Option 2] Regular expression (see str_subset). If neither words nor pattern are specified (i.e., both are NULL), then all words in the data will be extracted.

plot

Generate a plot to illustrate the word vectors? Defaults to FALSE.

plot.dims

Dimensions to be plotted (e.g., 1:100). Defaults to NULL (plot all dimensions).

plot.step

Step for value breaks. Defaults to 0.05.

plot.border

Color of tile border. Defaults to "white". To remove the border color, set plot.border=NA.

Value

A data.table with words as columns and dimensions as rows.

Download

Download pre-trained word vectors data (.RData): https://psychbruce.github.io/WordVector_RData.pdf

See also

data_wordvec_subset

plot_wordvec

plot_wordvec_tSNE

Examples

d = as_embed(demodata, normalize=TRUE)

get_wordvec(d, c("China", "Japan", "Korea"))
#>            China        Japan        Korea
#>   1: -0.02697318  0.018329469  0.033778005
#>   2:  0.04999036  0.090237829  0.133294179
#>   3:  0.04010034  0.060275982 -0.021963288
#>   4:  0.03056975  0.030490640  0.001675763
#>   5: -0.04711339 -0.095877693 -0.014011015
#>  ---                                      
#> 296: -0.01393625 -0.087418077 -0.072705831
#> 297: -0.06761304 -0.105042608 -0.115117643
#> 298:  0.05070960  0.048291315  0.015374394
#> 299:  0.03452576  0.002103985 -0.043017655
#> 300: -0.02931099  0.008636121  0.077552886
get_wordvec(d, cc(" China, Japan; Korea "))
#>            China        Japan        Korea
#>   1: -0.02697318  0.018329469  0.033778005
#>   2:  0.04999036  0.090237829  0.133294179
#>   3:  0.04010034  0.060275982 -0.021963288
#>   4:  0.03056975  0.030490640  0.001675763
#>   5: -0.04711339 -0.095877693 -0.014011015
#>  ---                                      
#> 296: -0.01393625 -0.087418077 -0.072705831
#> 297: -0.06761304 -0.105042608 -0.115117643
#> 298:  0.05070960  0.048291315  0.015374394
#> 299:  0.03452576  0.002103985 -0.043017655
#> 300: -0.02931099  0.008636121  0.077552886

## specify `pattern`:
get_wordvec(d, pattern="Chin[ae]|Japan|Korea")
#> 6 words matched...
#>            China        Japan     Chinese     Japanese      Korean        Korea
#>   1: -0.02697318  0.018329469 -0.04985132  0.003658937  0.02635512  0.033778005
#>   2:  0.04999036  0.090237829  0.04593451  0.091899336  0.11745195  0.133294179
#>   3:  0.04010034  0.060275982  0.07940573  0.062968012  0.03810029 -0.021963288
#>   4:  0.03056975  0.030490640  0.02581589  0.029782102  0.02850357  0.001675763
#>   5: -0.04711339 -0.095877693 -0.03204736 -0.093941412 -0.05127785 -0.014011015
#>  ---                                                                           
#> 296: -0.01393625 -0.087418077  0.04593451 -0.055820224 -0.05213705 -0.072705831
#> 297: -0.06761304 -0.105042608 -0.06907951 -0.119128757 -0.08479435 -0.115117643
#> 298:  0.05070960  0.048291315  0.06694317  0.074540472  0.02363348  0.015374394
#> 299:  0.03452576  0.002103985  0.05448025  0.001999554 -0.05614764 -0.043017655
#> 300: -0.02931099  0.008636121 -0.03275947  0.009104821  0.03867319  0.077552886

## plot word vectors:
get_wordvec(d, cc("China, Japan, Korea,
                   Mac, Linux, Windows"),
            plot=TRUE, plot.dims=1:100)

#>            China        Japan        Korea          Mac        Linux
#>   1: -0.02697318  0.018329469  0.033778005 -0.017270569  0.068922067
#>   2:  0.04999036  0.090237829  0.133294179 -0.012575617 -0.118698961
#>   3:  0.04010034  0.060275982 -0.021963288 -0.052985444 -0.071656892
#>   4:  0.03056975  0.030490640  0.001675763  0.078136677  0.030358376
#>   5: -0.04711339 -0.095877693 -0.014011015 -0.057009737 -0.045400893
#>  ---                                                                
#> 296: -0.01393625 -0.087418077 -0.072705831 -0.088532396 -0.001282133
#> 297: -0.06761304 -0.105042608 -0.115117643 -0.063716664  0.025572363
#> 298:  0.05070960  0.048291315  0.015374394 -0.005407505  0.072751102
#> 299:  0.03452576  0.002103985 -0.043017655 -0.016767489 -0.063451858
#> 300: -0.02931099  0.008636121  0.077552886  0.040912906  0.056341033
#>            Windows
#>   1:  0.0596512714
#>   2: -0.0797136210
#>   3: -0.0818534443
#>   4: -0.0143777935
#>   5: -0.0738285592
#>  ---              
#> 296:  0.0235396994
#> 297:  0.0005308471
#> 298:  0.0098973674
#> 299:  0.0572438332
#> 300:  0.1321425780

## a more complex example:

words = cc("
China
Chinese
Japan
Japanese
good
bad
great
terrible
morning
evening
king
queen
man
woman
he
she
cat
dog
")

dt = get_wordvec(
  d, words,
  plot=TRUE,
  plot.dims=1:100,
  plot.step=0.06)


# if you want to change something:
attr(dt, "ggplot") +
  scale_fill_viridis_b(n.breaks=10, show.limits=TRUE) +
  theme(legend.key.height=unit(0.1, "npc"))
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.


# or to save the plot:
ggsave(attr(dt, "ggplot"),
       filename="wordvecs.png",
       width=8, height=5, dpi=500)
unlink("wordvecs.png")  # delete file for code check