Tokenizer: Tokenizer objects

In NLP: Natural Language Processing Infrastructure

Tokenizer objects

Description

Create tokenizer objects.

Usage

Span_Tokenizer(f, meta = list())
as.Span_Tokenizer(x, ...)

Token_Tokenizer(f, meta = list())
as.Token_Tokenizer(x, ...)

Arguments

f	a tokenizer function taking the string to tokenize as argument, and returning either the tokens (for Token_Tokenizer) or their spans (for Span_Tokenizer).
meta	a named or empty list of tokenizer metadata tag-value pairs.
x	an R object.
...	further arguments passed to or from other methods.

Details

Tokenization is the process of breaking a text string up into words, phrases, symbols, or other meaningful elements called tokens. This can be accomplished by returning the sequence of tokens, or the corresponding spans (character start and end positions). We refer to tokenization resources of the respective kinds as “token tokenizers” and “span tokenizers”.

Span_Tokenizer() and Token_Tokenizer() return tokenizer objects which are functions with metadata and suitable class information, which in turn can be used for converting between the two kinds using as.Span_Tokenizer() or as.Token_Tokenizer(). It is also possible to coerce annotator (pipeline) objects to tokenizer objects, provided that the annotators provide suitable token annotations. By default, word tokens are used; this can be controlled via the type argument of the coercion methods (e.g., use type = "sentence" to extract sentence tokens).

There are also print() and format() methods for tokenizer objects, which use the description element of the metadata if available.

See Also

Regexp_Tokenizer() for creating regexp span tokenizers.

Examples

## A simple text.
s <- String("  First sentence.  Second sentence.  ")
##           ****5****0****5****0****5****0****5**

## Use a pre-built regexp (span) tokenizer:
wordpunct_tokenizer
wordpunct_tokenizer(s)
## Turn into a token tokenizer:
tt <- as.Token_Tokenizer(wordpunct_tokenizer)
tt
tt(s)
## Of course, in this case we could simply have done
s[wordpunct_tokenizer(s)]
## to obtain the tokens from the spans.
## Conversion also works the other way round: package 'tm' provides
## the following token tokenizer function:
scan_tokenizer <- function(x)
    scan(text = as.character(x), what = "character", quote = "", 
         quiet = TRUE)
## Create a token tokenizer from this:
tt <- Token_Tokenizer(scan_tokenizer)
tt(s)
## Turn into a span tokenizer:
st <- as.Span_Tokenizer(tt)
st(s)
## Checking tokens from spans:
s[st(s)]

NLP documentation built on Sept. 11, 2024, 6:59 p.m.