Package 'gibasa' reference manual

Title:	An Alternative 'Rcpp' Wrapper of 'MeCab'
Description:	A plain 'Rcpp' wrapper of 'MeCab' that can segment Chinese, Japanese, and Korean text into tokens. The main goal of this package is to provide an alternative to 'tidytext' using morphological analysis.
Authors:	Akiru Kato [aut, cre], Shogo Ichinose [aut], Taku Kudo [aut], Jorge Nocedal [ctb], Nippon Telegraph and Telephone Corporation [cph]
Maintainer:	Akiru Kato <[email protected]>
License:	GPL (>= 3)
Version:	1.1.1.9002
Built:	2024-11-17 04:23:30 UTC
Source:	https://github.com/paithiov909/gibasa

Create a list of tokens

Description

Create a list of tokens

Usage

as_tokens(
  tbl,
  token_field = "token",
  pos_field = get_dict_features()[1],
  nm = NULL
)
as_tokens(
  tbl,
  token_field = "token",
  pos_field = get_dict_features()[1],
  nm = NULL
)

Arguments

`tbl`	A tibble of tokens out of `tokenize()`.
`token_field`	<`data-masked`> Column containing tokens.
`pos_field`	Column containing features that will be kept as the names of tokens. If you don't need them, give a `NULL` for this argument.
`nm`	Names of returned list. If left with `NULL`, "doc_id" field of `tbl` is used instead.

Value

A named list of tokens.

Examples

## Not run: 
tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
) |>
  prettify(col_select = "POS1") |>
  as_tokens()

## End(Not run)
## Not run: 
tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
) |>
  prettify(col_select = "POS1") |>
  as_tokens()

## End(Not run)

Bind importance of bigrams

Description

Calculates and binds the importance of bigrams and their synergistic average.

Usage

bind_lr(tbl, term = "token", lr_mode = c("n", "dn"), avg_rate = 1)
bind_lr(tbl, term = "token", lr_mode = c("n", "dn"), avg_rate = 1)

Arguments

`tbl`	A tidy text dataset.
`term`	<`data-masked`> Column containing terms.
`lr_mode`	Method for computing 'FL' and 'FR' values. `n` is equivalent to 'LN' and 'RN', and `dn` is equivalent to 'LDN' and 'RDN'.
`avg_rate`	Weight of the 'LR' value.

Details

The 'LR' value is the synergistic average of bigram importance that based on the words and their positions (left or right side).

Value

A data.frame.

Examples

## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
bind_lr(df) |>
  head()

## End(Not run)
## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
bind_lr(df) |>
  head()

## End(Not run)

Bind term frequency and inverse document frequency

Description

Calculates and binds the term frequency, inverse document frequency, and TF-IDF of the dataset. This function experimentally supports 4 types of term frequencies and 5 types of inverse document frequencies.

Usage

bind_tf_idf2(
  tbl,
  term = "token",
  document = "doc_id",
  n = "n",
  tf = c("tf", "tf2", "tf3", "itf"),
  idf = c("idf", "idf2", "idf3", "idf4", "df"),
  norm = FALSE,
  rmecab_compat = TRUE
)
bind_tf_idf2(
  tbl,
  term = "token",
  document = "doc_id",
  n = "n",
  tf = c("tf", "tf2", "tf3", "itf"),
  idf = c("idf", "idf2", "idf3", "idf4", "df"),
  norm = FALSE,
  rmecab_compat = TRUE
)

Arguments

`tbl`	A tidy text dataset.
`term`	<`data-masked`> Column containing terms.
`document`	<`data-masked`> Column containing document IDs.
`n`	<`data-masked`> Column containing document-term counts.
`tf`	Method for computing term frequency.
`idf`	Method for computing inverse document frequency.
`norm`	Logical; If passed as `TRUE`, TF-IDF values are normalized being divided with L2 norms.
`rmecab_compat`	Logical; If passed as `TRUE`, computes values while taking care of compatibility with 'RMeCab'. Note that 'RMeCab' always computes IDF values using term frequency rather than raw term counts, and thus TF-IDF values may be doubly affected by term frequency.

Details

Types of term frequency can be switched with tf argument:

tf is term frequency (not raw count of terms).
tf2 is logarithmic term frequency of which base is exp(1).
tf3 is binary-weighted term frequency.
itf is inverse term frequency. Use with idf="df".

Types of inverse document frequencies can be switched with idf argument:

idf is inverse document frequency of which base is 2, with smoothed. 'smoothed' here means just adding 1 to raw values after logarithmizing.
idf2 is global frequency IDF.
idf3 is probabilistic IDF of which base is 2.
idf4 is global entropy, not IDF in actual.
df is document frequency. Use with tf="itf".

Value

A data.frame.

Examples

## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
) |>
  dplyr::group_by(doc_id) |>
  dplyr::count(token) |>
  dplyr::ungroup()
bind_tf_idf2(df) |>
  head()

## End(Not run)
## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
) |>
  dplyr::group_by(doc_id) |>
  dplyr::count(token) |>
  dplyr::ungroup()
bind_tf_idf2(df) |>
  head()

## End(Not run)

Build system dictionary

Description

Builds a UTF-8 system dictionary from source dictionary files.

Usage

build_sys_dic(dic_dir, out_dir, encoding)
build_sys_dic(dic_dir, out_dir, encoding)

Arguments

`dic_dir`	Directory where the source dictionaries are located. This argument is passed as '-d' option argument.
`out_dir`	Directory where the binary dictionary will be written. This argument is passed as '-o' option argument.
`encoding`	Encoding of input csv files. This argument is passed as '-f' option argument.

Details

This function is a wrapper around dictionary compiler of 'MeCab'.

Running this function will create 4 files: 'char.bin', 'matrix.bin', 'sys.dic', and 'unk.dic' in out_dir.

To use these compiled dictionary, you also need create a dicrc file in out_dir. A dicrc file is included in source dictionaries, so you can just copy it to out_dir.

Value

A TRUE is invisibly returned if dictionary is successfully built.

Examples


if (requireNamespace("withr")) {
  # create a sample dictionary in temporary directory
  build_sys_dic(
    dic_dir = system.file("latin", package = "gibasa"),
    out_dir = tempdir(),
    encoding = "utf8"
  )
  # copy the 'dicrc' file
  file.copy(
    system.file("latin/dicrc", package = "gibasa"),
    tempdir()
  )
  # mocking a 'mecabrc' file to temporarily use the dictionary
  withr::with_envvar(
    c(
      "MECABRC" = if (.Platform$OS.type == "windows") {
        "nul"
      } else {
        "/dev/null"
      },
      "RCPP_PARALLEL_BACKEND" = "tinythread"
    ),
    {
      tokenize("katta-wokattauresikatta", sys_dic = tempdir())
    }
  )
}

if (requireNamespace("withr")) {
  # create a sample dictionary in temporary directory
  build_sys_dic(
    dic_dir = system.file("latin", package = "gibasa"),
    out_dir = tempdir(),
    encoding = "utf8"
  )
  # copy the 'dicrc' file
  file.copy(
    system.file("latin/dicrc", package = "gibasa"),
    tempdir()
  )
  # mocking a 'mecabrc' file to temporarily use the dictionary
  withr::with_envvar(
    c(
      "MECABRC" = if (.Platform$OS.type == "windows") {
        "nul"
      } else {
        "/dev/null"
      },
      "RCPP_PARALLEL_BACKEND" = "tinythread"
    ),
    {
      tokenize("katta-wokattauresikatta", sys_dic = tempdir())
    }
  )
}

Build user dictionary

Description

Builds a UTF-8 user dictionary from a csv file.

Usage

build_user_dic(dic_dir, file, csv_file, encoding)
build_user_dic(dic_dir, file, csv_file, encoding)

Arguments

`dic_dir`	Directory where the source dictionaries are located. This argument is passed as '-d' option argument.
`file`	Path to write the user dictionary. This argument is passed as '-u' option argument.
`csv_file`	Path to an input csv file.
`encoding`	Encoding of input csv files. This argument is passed as '-f' option argument.

Details

This function is a wrapper around dictionary compiler of 'MeCab'.

Note that this function does not support auto assignment of word cost field. So, you can't leave any word costs as empty in your input csv file. To estimate word costs, use posDebugRcpp() function.

Value

A TRUE is invisibly returned if dictionary is successfully built.

Examples


if (requireNamespace("withr")) {
  # create a sample dictionary in temporary directory
  build_sys_dic(
    dic_dir = system.file("latin", package = "gibasa"),
    out_dir = tempdir(),
    encoding = "utf8"
  )
  # copy the 'dicrc' file
  file.copy(
    system.file("latin/dicrc", package = "gibasa"),
    tempdir()
  )
  # write a csv file and compile it into a user dictionary
  csv_file <- tempfile(fileext = ".csv")
  writeLines(
    c(
      "qa, 0, 0, 5, \u304f\u3041",
      "qi, 0, 0, 5, \u304f\u3043",
      "qu, 0, 0, 5, \u304f",
      "qe, 0, 0, 5, \u304f\u3047",
      "qo, 0, 0, 5, \u304f\u3049"
    ),
    csv_file
  )
  build_user_dic(
    dic_dir = tempdir(),
    file = (user_dic <- tempfile(fileext = ".dic")),
    csv_file = csv_file,
    encoding = "utf8"
  )
  # mocking a 'mecabrc' file to temporarily use the dictionary
  withr::with_envvar(
    c(
      "MECABRC" = if (.Platform$OS.type == "windows") {
        "nul"
      } else {
        "/dev/null"
      },
      "RCPP_PARALLEL_BACKEND" = "tinythread"
    ),
    {
      tokenize("quensan", sys_dic = tempdir(), user_dic = user_dic)
    }
  )
}

if (requireNamespace("withr")) {
  # create a sample dictionary in temporary directory
  build_sys_dic(
    dic_dir = system.file("latin", package = "gibasa"),
    out_dir = tempdir(),
    encoding = "utf8"
  )
  # copy the 'dicrc' file
  file.copy(
    system.file("latin/dicrc", package = "gibasa"),
    tempdir()
  )
  # write a csv file and compile it into a user dictionary
  csv_file <- tempfile(fileext = ".csv")
  writeLines(
    c(
      "qa, 0, 0, 5, \u304f\u3041",
      "qi, 0, 0, 5, \u304f\u3043",
      "qu, 0, 0, 5, \u304f",
      "qe, 0, 0, 5, \u304f\u3047",
      "qo, 0, 0, 5, \u304f\u3049"
    ),
    csv_file
  )
  build_user_dic(
    dic_dir = tempdir(),
    file = (user_dic <- tempfile(fileext = ".dic")),
    csv_file = csv_file,
    encoding = "utf8"
  )
  # mocking a 'mecabrc' file to temporarily use the dictionary
  withr::with_envvar(
    c(
      "MECABRC" = if (.Platform$OS.type == "windows") {
        "nul"
      } else {
        "/dev/null"
      },
      "RCPP_PARALLEL_BACKEND" = "tinythread"
    ),
    {
      tokenize("quensan", sys_dic = tempdir(), user_dic = user_dic)
    }
  )
}

Collapse sequences of tokens by condition

Description

Concatenates sequences of tokens in the tidy text dataset, while grouping them by an expression.

Usage

collapse_tokens(tbl, condition, .collapse = "")
collapse_tokens(tbl, condition, .collapse = "")

Arguments

`tbl`	A tidy text dataset.
`condition`	<`data-masked`> A logical expression.
`.collapse`	String with which tokens are concatenated.

Details

Note that this function drops all columns except but 'token' and columns for grouping sequences. So, the returned data.frame has only 'doc_id', 'sentence_id', 'token_id', and 'token' columns.

Value

A data.frame.

Examples

## Not run: 
df <- tokenize(
  data.frame(
    doc_id = "odakyu-sen",
    text = "\u5c0f\u7530\u6025\u7dda"
  )
) |>
  prettify(col_select = "POS1")

collapse_tokens(
  df,
  POS1 == "\u540d\u8a5e" & stringr::str_detect(token, "^[\\p{Han}]+$")
) |>
  head()

## End(Not run)
## Not run: 
df <- tokenize(
  data.frame(
    doc_id = "odakyu-sen",
    text = "\u5c0f\u7530\u6025\u7dda"
  )
) |>
  prettify(col_select = "POS1")

collapse_tokens(
  df,
  POS1 == "\u540d\u8a5e" & stringr::str_detect(token, "^[\\p{Han}]+$")
) |>
  head()

## End(Not run)

Get dictionary information

Description

Get dictionary information

Arguments

`sys_dic`	String scalar.
`user_dic`	String scalar.

Value

data.frame.

Tokenize sentences using 'MeCab'

Description

Tokenize sentences using 'MeCab'

Usage

gbs_tokenize(
  x,
  sys_dic = "",
  user_dic = "",
  split = FALSE,
  partial = FALSE,
  mode = c("parse", "wakati")
)
gbs_tokenize(
  x,
  sys_dic = "",
  user_dic = "",
  split = FALSE,
  partial = FALSE,
  mode = c("parse", "wakati")
)

Arguments

`x`	A data.frame like object or a character vector to be tokenized.
`sys_dic`	Character scalar; path to the system dictionary for 'MeCab'. Note that the system dictionary is expected to be compiled with UTF-8, not Shift-JIS or other encodings.
`user_dic`	Character scalar; path to the user dictionary for 'MeCab'.
`split`	Logical. When passed as `TRUE`, the function internally splits the sentences into sub-sentences using `stringi::stri_split_boundaries(type = "sentence")`.
`partial`	Logical. When passed as `TRUE`, activates partial parsing mode. To activate this feature, remember that all spaces at the start and end of the input chunks are already squashed. In particular, trailing spaces of chunks sometimes cause errors when parsing.
`mode`	Character scalar to switch output format.

Value

A tibble or a named list of tokens.

Get dictionary features

Description

Returns names of dictionary features. Currently supports "unidic17" (2.1.2 src schema), "unidic26" (2.1.2 bin schema), "unidic29" (schema used in 2.2.0, 2.3.0), "cc-cedict", "ko-dic" (mecab-ko-dic), "naist11", "sudachi", and "ipa".

Usage

get_dict_features(
  dict = c("ipa", "unidic17", "unidic26", "unidic29", "cc-cedict", "ko-dic", "naist11",
    "sudachi")
)
get_dict_features(
  dict = c("ipa", "unidic17", "unidic26", "unidic29", "cc-cedict", "ko-dic", "naist11",
    "sudachi")
)

Arguments

dict

Character scalar; one of "ipa", "unidic17", "unidic26", "unidic29", "cc-cedict", "ko-dic", "naist11", or "sudachi".

Value

A character vector.

Examples

get_dict_features("ipa")
get_dict_features("ipa")

Whole text of 'Ginga Tetsudo no Yoru' written by Miyazawa Kenji from Aozora Bunko

Description

Whole text of 'Ginga Tetsudo no Yoru' written by Miyazawa Kenji from Aozora Bunko

Usage

ginga
ginga

Format

An object of class character of length 553.

Details

A dataset containing the text of Miyazawa Kenji's novel "Ginga Tetsudo no Yoru" (English title: "Night on the Galactic Railroad") which was published in 1934, the year after Kenji's death. Copyright of this work has expired since more than 70 years have passed after the author's death.

The UTF-8 plain text is sourced from https://www.aozora.gr.jp/cards/000081/card43737.html and is cleaned of meta data.

Source

https://www.aozora.gr.jp/cards/000081/files/43737_ruby_19028.zip

Examples

head(ginga)
head(ginga)

Check if scalars are blank

Description

Check if scalars are blank

Usage

is_blank(x, trim = TRUE, ...)
is_blank(x, trim = TRUE, ...)

Arguments

`x`	Object to check its emptiness.
`trim`	Logical.
`...`	Additional arguments for `base::sapply()`.

Value

Logicals.

Examples

is_blank(list(c(a = "", b = NA_character_), NULL))
is_blank(list(c(a = "", b = NA_character_), NULL))

Calculate lexical density

Description

The lexical density is the proportion of content words (lexical items) in documents. This function is a simple helper for calculating the lexical density of given datasets.

Usage

lex_density(vec, contents_words, targets = NULL, negate = c(FALSE, FALSE))
lex_density(vec, contents_words, targets = NULL, negate = c(FALSE, FALSE))

Arguments

`vec`	A character vector.
`contents_words`	A character vector containing values to be counted as contents words.
`targets`	A character vector with which the denominator of lexical density is filtered before computing values.
`negate`	A logical vector of which length is 2. If passed as `TRUE`, then respectively negates the predicate functions for counting contents words or targets.

Value

A numeric vector.

Examples

## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
df |>
  prettify(col_select = "POS1") |>
  dplyr::group_by(doc_id) |>
  dplyr::summarise(
    noun_ratio = lex_density(POS1,
      "\u540d\u8a5e",
      c("\u52a9\u8a5e", "\u52a9\u52d5\u8a5e"),
      negate = c(FALSE, TRUE)
    ),
    mvr = lex_density(
      POS1,
      c("\u5f62\u5bb9\u8a5e", "\u526f\u8a5e", "\u9023\u4f53\u8a5e"),
      "\u52d5\u8a5e"
    ),
    vnr = lex_density(POS1, "\u52d5\u8a5e", "\u540d\u8a5e")
  )

## End(Not run)
## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
df |>
  prettify(col_select = "POS1") |>
  dplyr::group_by(doc_id) |>
  dplyr::summarise(
    noun_ratio = lex_density(POS1,
      "\u540d\u8a5e",
      c("\u52a9\u8a5e", "\u52a9\u52d5\u8a5e"),
      negate = c(FALSE, TRUE)
    ),
    mvr = lex_density(
      POS1,
      c("\u5f62\u5bb9\u8a5e", "\u526f\u8a5e", "\u9023\u4f53\u8a5e"),
      "\u52d5\u8a5e"
    ),
    vnr = lex_density(POS1, "\u52d5\u8a5e", "\u540d\u8a5e")
  )

## End(Not run)

Mute tokens by condition

Description

Replaces tokens in the tidy text dataset with a string scalar only if they are matched to an expression.

Usage

mute_tokens(tbl, condition, .as = NA_character_)
mute_tokens(tbl, condition, .as = NA_character_)

Arguments

`tbl`	A tidy text dataset.
`condition`	<`data-masked`> A logical expression.
`.as`	String with which tokens are replaced when they are matched to condition. The default value is `NA_character`.

Value

A data.frame.

Examples

## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
) |>
  prettify(col_select = "POS1")

mute_tokens(df, POS1 %in% c("\u52a9\u8a5e", "\u52a9\u52d5\u8a5e")) |>
  head()

## End(Not run)
## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
) |>
  prettify(col_select = "POS1")

mute_tokens(df, POS1 %in% c("\u52a9\u8a5e", "\u52a9\u52d5\u8a5e")) |>
  head()

## End(Not run)

Ngrams tokenizer

Description

Makes an ngram tokenizer function.

Usage

ngram_tokenizer(n = 1L)
ngram_tokenizer(n = 1L)

Arguments

n

Integer.

Value

ngram tokenizer function

Examples

bigram <- ngram_tokenizer(2)
bigram(letters, sep = "-")
bigram <- ngram_tokenizer(2)
bigram(letters, sep = "-")

Pack a data.frame of tokens

Description

Packs a data.frame of tokens into a new data.frame of corpus, which is compatible with the Text Interchange Formats.

Usage

pack(tbl, pull = "token", n = 1L, sep = "-", .collapse = " ")
pack(tbl, pull = "token", n = 1L, sep = "-", .collapse = " ")

Arguments

`tbl`	A data.frame of tokens.
`pull`	<`data-masked`> Column to be packed into text or ngrams body. Default value is `token`.
`n`	Integer internally passed to ngrams tokenizer function created of `gibasa::ngram_tokenizer()`
`sep`	Character scalar internally used as the concatenator of ngrams.
`.collapse`	This argument is passed to `stringi::stri_c()`.

Value

A tibble.

Text Interchange Formats (TIF)

The Text Interchange Formats (TIF) is a set of standards that allows R text analysis packages to target defined inputs and outputs for corpora, tokens, and document-term matrices.

Valid data.frame of tokens

The data.frame of tokens here is a data.frame object compatible with the TIF.

A TIF valid data.frame of tokens is expected to have one unique key column (named doc_id) of each text and several feature columns of each tokens. The feature columns must contain at least token itself.

Examples

## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
pack(df)

## End(Not run)
## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
pack(df)

## End(Not run)

Prettify tokenized output

Description

Turns a single character column into features while separating with delimiter.

Usage

prettify(
  tbl,
  col = "feature",
  into = get_dict_features("ipa"),
  col_select = seq_along(into),
  delim = ","
)
prettify(
  tbl,
  col = "feature",
  into = get_dict_features("ipa"),
  col_select = seq_along(into),
  delim = ","
)

Arguments

`tbl`	A data.frame that has feature column to be prettified.
`col`	<`data-masked`> Column containing features to be prettified.
`into`	Character vector that is used as column names of features.
`col_select`	Character or integer vector that will be kept in prettified features.
`delim`	Character scalar used to separate fields within a feature.

Value

A data.frame.

Examples

prettify(
  data.frame(x = c("x,y", "y,z", "z,x")),
  col = "x",
  into = c("a", "b"),
  col_select = "b"
)

## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
prettify(df, col_select = 1:3)
prettify(df, col_select = c(1, 3, 6))
prettify(df, col_select = c("POS1", "Original"))

## End(Not run)
prettify(
  data.frame(x = c("x,y", "y,z", "z,x")),
  col = "x",
  into = c("a", "b"),
  col_select = "b"
)

## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
prettify(df, col_select = 1:3)
prettify(df, col_select = c(1, 3, 6))
prettify(df, col_select = c("POS1", "Original"))

## End(Not run)

Tokenize sentences using 'MeCab'

Description

Tokenize sentences using 'MeCab'

Usage

tokenize(
  x,
  text_field = "text",
  docid_field = "doc_id",
  sys_dic = "",
  user_dic = "",
  split = FALSE,
  partial = FALSE,
  grain_size = 1L,
  mode = c("parse", "wakati")
)
tokenize(
  x,
  text_field = "text",
  docid_field = "doc_id",
  sys_dic = "",
  user_dic = "",
  split = FALSE,
  partial = FALSE,
  grain_size = 1L,
  mode = c("parse", "wakati")
)

Arguments

`x`	A data.frame like object or a character vector to be tokenized.
`text_field`	<`data-masked`> String or symbol; column containing texts to be tokenized.
`docid_field`	<`data-masked`> String or symbol; column containing document IDs.
`sys_dic`	Character scalar; path to the system dictionary for 'MeCab'. Note that the system dictionary is expected to be compiled with UTF-8, not Shift-JIS or other encodings.
`user_dic`	Character scalar; path to the user dictionary for 'MeCab'.
`split`	Logical. When passed as `TRUE`, the function internally splits the sentences into sub-sentences using `stringi::stri_split_boundaries(type = "sentence")`.
`partial`	Logical. When passed as `TRUE`, activates partial parsing mode. To activate this feature, remember that all spaces at the start and end of the input chunks are already squashed. In particular, trailing spaces of chunks sometimes cause errors when parsing.
`grain_size`	Integer value larger than 1. This argument is internally passed to `RcppParallel::parallelFor` function. Setting a larger chunk size could improve the performance in some cases.
`mode`	Character scalar to switch output format.

Value

A tibble or a named list of tokens.

Examples

## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
head(df)

## End(Not run)
## Not run: 
df <- tokenize(
  data.frame(
    doc_id = seq_along(5:8),
    text = ginga[5:8]
  )
)
head(df)

## End(Not run)

Package 'gibasa'

Help Index

Create a list of tokens

Description

Usage

Arguments

Value

Examples

Bind importance of bigrams

Description

Usage

Arguments

Details

Value

See Also

Examples

Bind term frequency and inverse document frequency

Description

Usage

Arguments

Details

Value

Examples

Build system dictionary

Description

Usage

Arguments

Details

Value

Examples

Build user dictionary

Description

Usage

Arguments

Details

Value

Examples

Collapse sequences of tokens by condition

Description

Usage

Arguments

Details

Value

Examples

Get dictionary information

Description

Arguments

Value

Tokenize sentences using 'MeCab'

Description

Usage

Arguments

Value

Get dictionary features

Description

Usage

Arguments

Value

See Also

Examples

Whole text of 'Ginga Tetsudo no Yoru' written by Miyazawa Kenji from Aozora Bunko

Description

Usage

Format

Details

Source

Examples

Check if scalars are blank

Description

Usage

Arguments

Value

Examples

Calculate lexical density

Description

Usage

Arguments

Value

Examples

Mute tokens by condition