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NLP (version 0.1-6)

TaggedTextDocument: POS-Tagged Word Text Documents

Description

Create text documents from files containing POS-tagged words.

Usage

TaggedTextDocument(con, encoding = "unknown",
                   word_tokenizer = whitespace_tokenizer,
                   sent_tokenizer = Regexp_Tokenizer("", invert = TRUE),
                   para_tokenizer = blankline_tokenizer,
                   sep = "/",
                   meta = list())
con{a connection object or a character string.
    See readLines() for details.
  }
  encoding{encoding to be assumed for input strings.
    See readLines() for details.
  }
  word_tokenizer{a function for obtaining the word token spans.}
  sent_tokenizer{a function for obtaining the sentence token
    spans.}
  para_tokenizer{a function for obtaining the paragraph token
    spans, or NULL in which case no paragraph tokenization is
    performed.}
  sep{the character string separating the word tokens and their
    POS tags.}
  meta{a named or empty list of document metadata tag-value
    pairs.}

TaggedTextDocument() creates documents representing natural
  language text as suitable collections of POS-tagged words, based on
  using readLines() to read text lines from connections
  providing such collections.
  The text read is split into paragraph, sentence and tagged word tokens
  using the span tokenizers specified by arguments
  para_tokenizer, sent_tokenizer and
  word_tokenizer.  By default, paragraphs are assumed to be
  separated by blank lines, sentences by newlines and tagged word tokens
  by whitespace.  Finally, word tokens and their POS tags are obtained
  by splitting the tagged word tokens according to sep.  From
  this, a suitable representation of the provided collection of
  POS-tagged words is obtained, and returned as a tagged text document
  object inheriting from classes "TaggedTextDocument" and
  "TextDocument".
  There are methods for generics
  words(),
  sents(),
  paras(),
  tagged_words(),
  tagged_sents(), and
  tagged_paras()
  (as well as as.character())
  and class "TaggedTextDocument",
  which should be used to access the text in such text document
  objects.

An object inheriting from "TaggedTextDocument" and
  "TextDocument".
http://nltk.github.com/nltk_data/packages/corpora/brown.zip
  which provides the W. N. Francis and H. Kucera Brown tagged word
  corpus as an archive of files which can be read in using
  TaggedWordDocument().

Arguments