quanteda (version 1.5.1)

corpus_segment: Segment texts on a pattern match

Description

Segment corpus text(s) or a character vector, splitting on a pattern match. This is useful for breaking the texts into smaller documents based on a regular pattern (such as a speaker identifier in a transcript) or a user-supplied annotation.

Usage

corpus_segment(x, pattern = "##*", valuetype = c("glob", "regex",
  "fixed"), case_insensitive = TRUE, extract_pattern = TRUE,
  pattern_position = c("before", "after"), use_docvars = TRUE)
char_segment(x, pattern = "##*", valuetype = c("glob", "regex",
  "fixed"), case_insensitive = TRUE, remove_pattern = TRUE,
  pattern_position = c("before", "after"))

Arguments

x

character or corpus object whose texts will be segmented

pattern

a character vector, list of character vectors, dictionary, or collocations object. See pattern for details.

valuetype

the type of pattern matching: "glob" for "glob"-style wildcard expressions; "regex" for regular expressions; or "fixed" for exact matching. See valuetype for details.

case_insensitive

ignore case when matching, if TRUE

extract_pattern

extracts matched patterns from the texts and save in docvars if TRUE

pattern_position

either "before" or "after", depending on whether the pattern precedes the text (as with a user-supplied tag, such as ##INTRO in the examples below) or follows the text (as with punctuation delimiters)

use_docvars

if TRUE, repeat the docvar values for each segmented text; if FALSE, drop the docvars in the segmented corpus. Dropping the docvars might be useful in order to conserve space or if these are not desired for the segmented corpus.

remove_pattern

removes matched patterns from the texts if TRUE

Value

corpus_segment returns a corpus of segmented texts

char_segment returns a character vector of segmented texts

Boundaries and segmentation explained

The pattern acts as a boundary delimiter that defines the segmentation points for splitting a text into new "document" units. Boundaries are always defined as the pattern matches, plus the end and beginnings of each document. The new "documents" that are created following the segmentation will then be the texts found between boundaries.

The pattern itself will be saved as a new document variable named pattern. This is most useful when segmenting a text according to tags such as names in a transcript, section titles, or user-supplied annotations. If the beginning of the file precedes a pattern match, then the extracted text will have a NA for the extracted pattern document variable (or when pattern_position = "after", this will be true for the text split between the last pattern match and the end of the document).

To extract syntactically defined sub-document units such as sentences and paragraphs, use corpus_reshape instead.

Using patterns

One of the most common uses for corpus_segment is to partition a corpus into sub-documents using tags. The default pattern value is designed for a user-annotated tag that is a term beginning with double "hash" signs, followed by a whitespace, for instance as ##INTRODUCTION The text.

Glob and fixed pattern types use a whitespace character to signal the end of the pattern.

For more advanced pattern matches that could include whitespace or newlines, a regex pattern type can be used, for instance a text such as

Mr. Smith: Text Mrs. Jones: More text

could have as pattern = "\\b[A-Z].+\\.\\s[A-Z][a-z]+:", which would catch the title, the name, and the colon.

For custom boundary delimitation using punctuation characters that come come at the end of a clause or sentence (such as , and., these can be specified manually and pattern_position set to "after". To keep the punctuation characters in the text (as with sentence segmentation), set extract_pattern = FALSE. (With most tag applications, users will want to remove the patterns from the text, as they are annotations rather than parts of the text itself.)

Details

For segmentation into syntactic units defined by the locale (such as sentences), use corpus_reshape instead. In cases where more fine-grained segmentation is needed, such as that based on commas or semi-colons (phrase delimiters within a sentence), corpus_segment offers greater user control than corpus_reshape.

See Also

corpus_reshape, for segmenting texts into pre-defined syntactic units such as sentences, paragraphs, or fixed-length chunks

Examples

# NOT RUN {
## segmenting a corpus

# segmenting a corpus using tags
corp1 <- corpus(c("##INTRO This is the introduction.
                  ##DOC1 This is the first document.  Second sentence in Doc 1.
                  ##DOC3 Third document starts here.  End of third document.",
                 "##INTRO Document ##NUMBER Two starts before ##NUMBER Three."))
corpseg1 <- corpus_segment(corp1, pattern = "##*")
cbind(texts(corpseg1), docvars(corpseg1), metadoc(corpseg1))

# segmenting a transcript based on speaker identifiers
corp2 <- corpus("Mr. Smith: Text.\nMrs. Jones: More text.\nMr. Smith: I'm speaking, again.")
corpseg2 <- corpus_segment(corp2, pattern = "\\b[A-Z].+\\s[A-Z][a-z]+:",
                            valuetype = "regex")
cbind(texts(corpseg2), docvars(corpseg2), metadoc(corpseg2))

# segmenting a corpus using crude end-of-sentence segmentation
corpseg3 <- corpus_segment(corp1, pattern = ".", valuetype = "fixed", 
                            pattern_position = "after", extract_pattern = FALSE)
cbind(texts(corpseg3), docvars(corpseg3), metadoc(corpseg3))

## segmenting a character vector

# segment into paragraphs and removing the "- " bullet points
cat(data_char_ukimmig2010[4])
char_segment(data_char_ukimmig2010[4], 
             pattern = "\\n\\n(\\-\\s){0,1}", valuetype = "regex", 
             remove_pattern = TRUE)

# segment a text into clauses
txt <- c(d1 = "This, is a sentence?  You: come here.", d2 = "Yes, yes okay.")
char_segment(txt, pattern = "\\p{P}", valuetype = "regex", 
             pattern_position = "after", remove_pattern = FALSE)
# }