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\name{viewers}
\alias{sents}
\alias{words}
\alias{paras}
\alias{tagged_sents}
\alias{tagged_paras}
\alias{tagged_words}
\alias{chunked_sents}
\alias{parsed_sents}
\alias{parsed_paras}
\alias{otoks}
\title{Text Document Viewers}
\description{
Provide suitable \dQuote{views} of the text contained in text
documents.
}
\usage{
words(x, ...)
sents(x, ...)
paras(x, ...)
tagged_words(x, ...)
tagged_sents(x, ...)
tagged_paras(x, ...)
chunked_sents(x, ...)
parsed_sents(x, ...)
parsed_paras(x, ...)
}
\arguments{
\item{x}{a text document object.}
\item{...}{further arguments to be passed to or from methods.}
}
\details{
Methods for extracting POS tagged word tokens (i.e., for generics
\code{tagged_words()}, \code{tagged_sents()} and
\code{tagged_paras()}) can optionally provide a mechanism for mapping
the POS tags via a \code{map} argument. This can give a function, a
named character vector (with names and elements the tags to map from
and to, respectively), or a named list of such named character
vectors, with names corresponding to POS tagsets (see
\code{\link{Universal_POS_tags_map}} for an example). If a list, the
map used will be the element with name matching the POS tagset used
(this information is typically determined from the text document
metadata; see the the help pages for text document extension classes
implementing this mechanism for details).
Text document classes may provide support for representing both
(syntactic) words (for which annotations can be provided) and
orthographic (word) tokens, e.g., in Spanish \emph{dámelo = da me lo}.
For these, \code{words()} gives the syntactic word tokens, and
\code{otoks()} the orthographic word tokens. This is currently
supported for \link[=CoNLLUTextDocument]{CoNNL-U text documents} (see
\url{https://universaldependencies.org/format.html} for more
information) and \link[=AnnotatedPlainTextDocument]{annotated plain
text documents} (via \code{word} features as used for example for some
Stanford CoreNLP annotator pipelines provided by package
\pkg{StanfordCoreNLP} available from the repository at
\url{https://datacube.wu.ac.at}).
In addition to methods for the text document classes provided by
package \pkg{NLP} itself, (see \link{TextDocument}), package \pkg{NLP}
also provides word tokens and POS tagged word tokens for the results
of
\code{\link[udpipe]{udpipe_annotate}()}
from package \CRANpkg{udpipe},
\code{\link[spacyr]{spacy_parse}()}
from package \CRANpkg{spacyr},
and
\code{\link[cleanNLP]{cnlp_annotate}()}
from package \CRANpkg{cleanNLP}.
}
\value{
For \code{words()}, a character vector with the word tokens in the
document.
For \code{sents()}, a list of character vectors with the word tokens
in the sentences.
For \code{paras()}, a list of lists of character vectors with the word
tokens in the sentences, grouped according to the paragraphs.
For \code{tagged_words()}, a character vector with the POS tagged word
tokens in the document (i.e., the word tokens and their POS tags,
separated by \samp{/}).
For \code{tagged_sents()}, a list of character vectors with the POS
tagged word tokens in the sentences.
For \code{tagged_paras()}, a list of lists of character vectors with
the POS tagged word tokens in the sentences, grouped according to the
paragraphs.
For \code{chunked_sents()}, a list of (flat) \code{\link{Tree}}
objects giving the chunk trees for the sentences in the document.
For \code{parsed_sents()}, a list of \code{\link{Tree}}
objects giving the parse trees for the sentences in the document.
For \code{parsed_paras()}, a list of lists of \code{\link{Tree}}
objects giving the parse trees for the sentences in the document,
grouped according to the paragraphs in the document.
For \code{otoks()}, a character vector with the orthographic word
tokens in the document.
}
\seealso{
\code{\link{TextDocument}} for basic information on the text document
infrastructure employed by package \pkg{NLP}.
}
\examples{
## Example from <https://universaldependencies.org/format.html>:
d <- CoNLLUTextDocument(system.file("texts", "spanish.conllu",
package = "NLP"))
content(d)
## To extract the syntactic words:
words(d)
## To extract the orthographic word tokens:
otoks(d)
}
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