Extract a subset of word vectors data (with S3 methods).

Extract a subset of word vectors data (with S3 methods). You may specify either a wordvec or embed loaded by data_wordvec_load) or an .RData file transformed by data_transform).

Usage

data_wordvec_subset(
  x,
  words = NULL,
  pattern = NULL,
  as = c("wordvec", "embed"),
  file.save,
  compress = "bzip2",
  compress.level = 9,
  verbose = TRUE
)

# S3 method for wordvec
subset(x, ...)

# S3 method for embed
subset(x, ...)

Arguments

x

Can be:

a wordvec or embed loaded by data_wordvec_load
an .RData file transformed by data_transform

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.

as

Reshape to wordvec (data.table) or embed (matrix). Defaults to the original class of x.

file.save

File name of to-be-saved R data (must be .RData).

compress

Compression method for the saved file. Defaults to "bzip2".

Options include:

1 or "gzip": modest file size (fastest)
2 or "bzip2": small file size (fast)
3 or "xz": minimized file size (slow)

compress.level

Compression level from 0 (none) to 9 (maximal compression for minimal file size). Defaults to 9.

verbose

Print information to the console? Defaults to TRUE.

...

Parameters passed to data_wordvec_subset when using the S3 method subset.

Value

A subset of wordvec or embed of valid (available) words.

Download

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

Examples

## directly use `embed[i, j]` (3x faster than `wordvec`):
d = as_embed(demodata)
d[1:5]
#> # embed (matrix): [5 × 300] (NOT normalized)
#>            dim1 ...     dim300
#> 1: in    0.0703 ... <300 dims>
#> 2: for  -0.0118 ... <300 dims>
#> 3: that -0.0157 ... <300 dims>
#> 4: is    0.0070 ... <300 dims>
#> 5: on    0.0267 ... <300 dims>
d["people"]
#> # embed (matrix): [1 × 300] (NOT normalized)
#>              dim1 ...     dim300
#> 1: people  0.2637 ... <300 dims>
d[c("China", "Japan", "Korea")]
#> # embed (matrix): [3 × 300] (NOT normalized)
#>             dim1 ...     dim300
#> 1: China -0.0732 ... <300 dims>
#> 2: Japan  0.0508 ... <300 dims>
#> 3: Korea  0.1089 ... <300 dims>

## specify `x` as a `wordvec` or `embed` object:
subset(demodata, c("China", "Japan", "Korea"))
#> # wordvec (data.table): [3 × 2] (NOT normalized)
#>     word                      vec
#> 1: China [-0.0732, ...<300 dims>]
#> 2: Japan [ 0.0508, ...<300 dims>]
#> 3: Korea [ 0.1089, ...<300 dims>]
subset(d, pattern="^Chi")
#> 4 words matched...
#> # embed (matrix): [4 × 300] (NOT normalized)
#>               dim1 ...     dim300
#> 1: China   -0.0732 ... <300 dims>
#> 2: Chicago -0.0786 ... <300 dims>
#> 3: Chinese -0.1367 ... <300 dims>
#> 4: Chile   -0.2754 ... <300 dims>

## specify `x` and `pattern`, and save with `file.save`:
subset(demodata, pattern="Chin[ae]|Japan|Korea",
       file.save="subset.RData")
#> 6 words matched...
#> 
#> Compressing and saving...
#> ✔ Saved to subset.RData (time cost = 0.004 secs)
#> # wordvec (data.table): [6 × 2] (NOT normalized)
#>        word                      vec
#> 1:    China [-0.0732, ...<300 dims>]
#> 2:    Japan [ 0.0508, ...<300 dims>]
#> 3:  Chinese [-0.1367, ...<300 dims>]
#> 4: Japanese [ 0.0105, ...<300 dims>]
#> 5:   Korean [ 0.0898, ...<300 dims>]
#> 6:    Korea [ 0.1089, ...<300 dims>]

## load the subset:
d.subset = load_wordvec("subset.RData")
#> Loading...

#> ✔ Word vectors data: 6 vocab, 300 dims (time cost = 0.002 secs)
#> ✔ All word vectors have been normalized to unit length 1.
d.subset
#> # wordvec (data.table): [6 × 2] (normalized)
#>        word                      vec
#> 1:    China [-0.0270, ...<300 dims>]
#> 2:    Japan [ 0.0183, ...<300 dims>]
#> 3:  Chinese [-0.0499, ...<300 dims>]
#> 4: Japanese [ 0.0037, ...<300 dims>]
#> 5:   Korean [ 0.0264, ...<300 dims>]
#> 6:    Korea [ 0.0338, ...<300 dims>]

## specify `x` as an .RData file and save with `file.save`:
data_wordvec_subset("subset.RData",
                    words=c("China", "Chinese"),
                    file.save="new.subset.RData")
#> Loading...

#> ✔ Word vectors data: 6 vocab, 300 dims (time cost = 0.002 secs)
#> 
#> Compressing and saving...
#> ✔ Saved to new.subset.RData (time cost = 0.003 secs)
#> # wordvec (data.table): [2 × 2] (NOT normalized)
#>       word                      vec
#> 1:   China [-0.0732, ...<300 dims>]
#> 2: Chinese [-0.1367, ...<300 dims>]
d.new.subset = load_embed("new.subset.RData")
#> Loading...

#> ✔ Word vectors data: 2 vocab, 300 dims (time cost = 0.002 secs)
#> ✔ All word vectors have been normalized to unit length 1.
d.new.subset
#> # embed (matrix): [2 × 300] (normalized)
#>               dim1 ...     dim300
#> 1: China   -0.0270 ... <300 dims>
#> 2: Chinese -0.0499 ... <300 dims>

unlink("subset.RData")  # delete file for code check
unlink("new.subset.RData")  # delete file for code check

Usage

Arguments

Value

Download

See also

Examples