Package 'NLP'

Annotate text strings

Description

Compute annotations by iteratively calling the given annotators with the given text and current annotations, and merging the newly computed annotations with the current ones.

Usage

annotate(s, f, a = Annotation())

Arguments

s	a String object, or something coercible to this using as.String (e.g., a character string with appropriate encoding information).
f	an Annotator or Annotator_Pipeline object, or something coercible to the latter via as.Annotator_Pipeline() (such as a list of annotator objects).
a	an Annotation object giving the annotations to start with.

Value

An Annotation object containing the iteratively computed and merged annotations.

Examples

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

## A very trivial sentence tokenizer.
sent_tokenizer <-
function(s) {
    s <- as.String(s)
    m <- gregexpr("[^[:space:]][^.]*\\.", s)[[1L]]
    Span(m, m + attr(m, "match.length") - 1L)
}
## (Could also use Regexp_Tokenizer() with the above regexp pattern.)
## A simple sentence token annotator based on the sentence tokenizer.
sent_token_annotator <- Simple_Sent_Token_Annotator(sent_tokenizer)

## Annotate sentence tokens.
a1 <- annotate(s, sent_token_annotator)
a1

## A very trivial word tokenizer.
word_tokenizer <-
function(s) {
    s <- as.String(s)
    ## Remove the last character (should be a period when using
    ## sentences determined with the trivial sentence tokenizer).
    s <- substring(s, 1L, nchar(s) - 1L)
    ## Split on whitespace separators.
    m <- gregexpr("[^[:space:]]+", s)[[1L]]
    Span(m, m + attr(m, "match.length") - 1L)
}
## A simple word token annotator based on the word tokenizer.
word_token_annotator <- Simple_Word_Token_Annotator(word_tokenizer)

## Annotate word tokens using the already available sentence token
## annotations.
a2 <- annotate(s, word_token_annotator, a1)
a2

## Can also perform sentence and word token annotations in a pipeline:
p <- Annotator_Pipeline(sent_token_annotator, word_token_annotator)
annotate(s, p)

---

Annotated Plain Text Documents

Description

Create annotated plain text documents from plain text and collections of annotations for this text.

Usage

AnnotatedPlainTextDocument(s, a, meta = list())
annotation(x)

Arguments

s	a String object, or something coercible to this using as.String() (e.g., a character string with appropriate encoding information).
a	an Annotation object with annotations for s.
meta	a named or empty list of document metadata tag-value pairs.
x	an object inheriting from class "AnnotatedPlainTextDocument".

Details

Annotated plain text documents combine plain text with annotations for the text.

A typical workflow is to use annotate() with suitable annotator pipelines to obtain the annotations, and then use AnnotatedPlainTextDocument() to combine these with the text being annotated. This yields an object inheriting from "AnnotatedPlainTextDocument" and "TextDocument", from which the text and annotations can be obtained using, respectively, as.character() and annotation().

There are methods for class "AnnotatedPlainTextDocument" and generics words(), sents(), paras(), tagged_words(), tagged_sents(), tagged_paras(), chunked_sents(), parsed_sents() and parsed_paras() providing structured views of the text in such documents. These all require the necessary annotations to be available in the annotation object used.

The methods for generics tagged_words(), tagged_sents() and tagged_paras() provide a mechanism for mapping POS tags via the map argument, see section Details in the help page for tagged_words() for more information. The POS tagset used will be inferred from the POS_tagset metadata element of the annotation object used.

Value

For AnnotatedPlainTextDocument(), an annotated plain text document object inheriting from "AnnotatedPlainTextTextDocument" and "TextDocument".

For annotation(), an Annotation object.

See Also

TextDocument for basic information on the text document infrastructure employed by package NLP.

Examples

## Use a pre-built annotated plain text document obtained by employing an
## annotator pipeline from package 'StanfordCoreNLP', available from the
## repository at <https://datacube.wu.ac.at>, using the following code:
##   require("StanfordCoreNLP")
##   s <- paste("Stanford University is located in California.",
##              "It is a great university.")
##   p <- StanfordCoreNLP_Pipeline(c("pos", "lemma", "parse"))
##   d <- AnnotatedPlainTextDocument(s, p(s))

d <- readRDS(system.file("texts", "stanford.rds", package = "NLP"))

d

## Extract available annotation:
a <- annotation(d)
a

## Structured views:
sents(d)
tagged_sents(d)
tagged_sents(d, map = Universal_POS_tags_map)
parsed_sents(d)

## Add (trivial) paragraph annotation:
s <- as.character(d)
a <- annotate(s, Simple_Para_Token_Annotator(blankline_tokenizer), a)
d <- AnnotatedPlainTextDocument(s, a)
## Structured view:
paras(d)

---

Annotation objects

Description

Creation and manipulation of annotation objects.

Usage

Annotation(id = NULL, type = NULL, start, end, features = NULL,
           meta = list())
as.Annotation(x, ...)
## S3 method for class 'Span'
as.Annotation(x, id = NULL, type = NULL, ...)
is.Annotation(x)

Arguments

id	an integer vector giving the annotation ids, or NULL (default) resulting in missing ids.
type	a character vector giving the annotation types, or NULL (default) resulting in missing types.
start, end	integer vectors giving the start and end positions of the character spans the annotations refer to.
features	a list of (named or empty) feature lists, or NULL (default), resulting in empty feature lists.
meta	a named or empty list of annotation metadata tag-value pairs.
x	an R object (an object of class "Span" for the coercion methods for such objects).
...	further arguments passed to or from other methods.

Details

A single annotation (of natural language text) is a quintuple with “slots” ‘id’, ‘type’, ‘start’, ‘end’, and ‘features’. These give, respectively, id and type, the character span the annotation refers to, and a collection of annotation features (tag/value pairs).

Annotation objects provide sequences (allowing positional access) of single annotations, together with metadata about these. They have class "Annotation" and, as they contain character spans, also inherit from class "Span". Span objects can be coerced to annotation objects via as.Annotation() which allows to specify ids and types (using the default values sets these to missing), and annotation objects can be coerced to span objects using as.Span().

The features of a single annotation are represented as named or empty lists.

Subscripting annotation objects via [ extracts subsets of annotations; subscripting via $ extracts the sequence of values of the named slot, i.e., an integer vector for ‘id’, ‘start’, and ‘end’, a character vector for ‘type’, and a list of named or empty lists for ‘features’.

There are several additional methods for class "Annotation": print() and format() (which both have a values argument which if FALSE suppresses indicating the feature map values); c() combines annotations (or objects coercible to these using as.Annotation()); merge() merges annotations by combining the feature lists of annotations with otherwise identical slots; subset() allows subsetting by expressions involving the slot names; and as.list() and as.data.frame() coerce, respectively, to lists (of single annotation objects) and data frames (with annotations and slots corresponding to rows and columns).

Annotation() creates annotation objects from the given sequences of slot values: those not NULL must all have the same length (the number of annotations in the object).

as.Annotation() coerces to annotation objects, with a method for span objects.

is.Annotation() tests whether an object inherits from class "Annotation".

Value

For Annotation() and as.Annotation(), an annotation object (of class "Annotation" also inheriting from class "Span").

For is.Annotation(), a logical.

Examples

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

## Basic sentence and word token annotations for the text.
a1s <- Annotation(1 : 2,
                  rep.int("sentence", 2L),
                  c( 3L, 20L),
                  c(17L, 35L))
a1w <- Annotation(3 : 6,
                  rep.int("word", 4L),
                  c( 3L,  9L, 20L, 27L),
                  c( 7L, 16L, 25L, 34L))

## Use c() to combine these annotations:
a1 <- c(a1s, a1w)
a1
## Subscripting via '[':
a1[3 : 4]
## Subscripting via '$':
a1$type
## Subsetting according to slot values, directly:
a1[a1$type == "word"]
## or using subset():
subset(a1, type == "word")

## We can subscript string objects by annotation objects to extract the
## annotated substrings:
s[subset(a1, type == "word")]
## We can also subscript by lists of annotation objects:
s[annotations_in_spans(subset(a1, type == "word"),
                       subset(a1, type == "sentence"))]

## Suppose we want to add the sentence constituents (the ids of the
## words in the respective sentences) to the features of the sentence
## annotations.  The basic computation is
lapply(annotations_in_spans(a1[a1$type == "word"],
                            a1[a1$type == "sentence"]),
       function(a) a$id)
## For annotations, we need lists of feature lists:
features <-
    lapply(annotations_in_spans(a1[a1$type == "word"],
                                a1[a1$type == "sentence"]),
           function(e) list(constituents = e$id))
## Could add these directly:
a2 <- a1
a2$features[a2$type == "sentence"] <- features
a2
## Note how the print() method summarizes the features.
## We could also write a sentence constituent annotator
## (note that annotators should always have formals 's' and 'a', even
## though for computing the sentence constituents s is not needed):
sent_constituent_annotator <-
Annotator(function(s, a) {
              i <- which(a$type == "sentence")
              features <-
                  lapply(annotations_in_spans(a[a$type == "word"],
                                              a[i]),
                        function(e) list(constituents = e$id))
              Annotation(a$id[i], a$type[i], a$start[i], a$end[i],
                         features)
         })
sent_constituent_annotator(s, a1)
## Can use merge() to merge the annotations:
a2 <- merge(a1, sent_constituent_annotator(s, a1))
a2
## Equivalently, could have used
a2 <- annotate(s, sent_constituent_annotator, a1)
a2
## which merges automatically.

---

Annotations contained in character spans

Description

Extract annotations contained in character spans.

Usage

annotations_in_spans(x, y)

Arguments

x	an Annotation object.
y	a Span object, or something coercible to this (such as an Annotation object).

Value

A list with elements the annotations in x with character spans contained in the respective elements of y.

Examples

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

## Basic sentence and word token annotation for the text.
a <- c(Annotation(1 : 2,
                  rep.int("sentence", 2L),
                  c( 3L, 20L),
                  c(17L, 35L)),
       Annotation(3 : 6,
                  rep.int("word", 4L),
                  c( 3L,  9L, 20L, 27L),
                  c( 7L, 16L, 25L, 34L)))

## Annotation for word tokens according to sentence:
annotations_in_spans(a[a$type == "word"], a[a$type == "sentence"])

---

Annotator (pipeline) objects

Description

Create annotator (pipeline) objects.

Usage

Annotator(f, meta = list(), classes = NULL)
Annotator_Pipeline(..., meta = list())
as.Annotator_Pipeline(x)

Arguments

f	an annotator function, which must have formals s and a giving, respectively, the string with the natural language text to annotate and an annotation object to start from, and return an annotation object with the computed annotations.
meta	an empty or named list of annotator (pipeline) metadata tag-value pairs.
classes	a character vector or NULL (default) giving classes to be used for the created annotator object in addition to "Annotator".
...	annotator objects.
x	an R object.

Details

Annotator() checks that the given annotator function has the appropriate formals, and returns an annotator object which inherits from the given classes and "Annotator". There are print() and format() methods for such objects, which use the description element of the metadata if available.

Annotator_Pipeline() creates an annotator pipeline object from the given annotator objects. Such pipeline objects can be used by annotate() for successively computing and merging annotations, and can also be obtained by coercion with as.Annotator_Pipeline(), which currently handles annotator objects and lists of such (and of course, annotator pipeline objects).

Value

For Annotator(), an annotator object inheriting from the given classes and class "Annotator".

For Annotator_Pipeline() and as.Annotator_Pipeline(), an annotator pipeline object inheriting from class "Annotator_Pipeline".

See Also

Simple annotator generators for creating “simple” annotator objects based on function performing simple basic NLP tasks.

Package StanfordCoreNLP available from the repository at https://datacube.wu.ac.at which provides generators for annotator pipelines based on the Stanford CoreNLP tools.

Examples

## Use blankline_tokenizer() for a simple paragraph token annotator:
para_token_annotator <-
Annotator(function(s, a = Annotation()) {
              spans <- blankline_tokenizer(s)
              n <- length(spans)
              ## Need n consecutive ids, starting with the next "free"
              ## one:
              from <- next_id(a$id)
              Annotation(seq(from = from, length.out = n),
                         rep.int("paragraph", n),
                         spans$start,
                         spans$end)
          },
          list(description = 
              "A paragraph token annotator based on blankline_tokenizer()."))
para_token_annotator
## Alternatively, use Simple_Para_Token_Annotator().

## A simple text with two paragraphs:
s <- String(paste("  First sentence.  Second sentence.  ",
                  "  Second paragraph.  ",
                  sep = "\n\n"))
a <- annotate(s, para_token_annotator)
## Annotations for paragraph tokens.
a
## Extract paragraph tokens.
s[a]

---

Simple annotator generators

Description

Create annotator objects for composite basic NLP tasks based on functions performing simple basic tasks.

Usage

Simple_Para_Token_Annotator(f, meta = list(), classes = NULL)
Simple_Sent_Token_Annotator(f, meta = list(), classes = NULL)
Simple_Word_Token_Annotator(f, meta = list(), classes = NULL)
Simple_POS_Tag_Annotator(f, meta = list(), classes = NULL)
Simple_Entity_Annotator(f, meta = list(), classes = NULL)
Simple_Chunk_Annotator(f, meta = list(), classes = NULL)
Simple_Stem_Annotator(f, meta = list(), classes = NULL)

Arguments

f	a function performing a “simple” basic NLP task (see Details).
meta	an empty or named list of annotator (pipeline) metadata tag-value pairs.
classes	a character vector or NULL (default) giving classes to be used for the created annotator object in addition to the default ones (see Details).

Details

The purpose of these functions is to facilitate the creation of annotators for basic NLP tasks as described below.

Simple_Para_Token_Annotator() creates “simple” paragraph token annotators. Argument f should be a paragraph tokenizer, which takes a string s with the whole text to be processed, and returns the spans of the paragraphs in s, or an annotation object with these spans and (possibly) additional features. The generated annotator inherits from the default classes "Simple_Para_Token_Annotator" and "Annotator". It uses the results of the simple paragraph tokenizer to create and return annotations with unique ids and type ‘paragraph’.

Simple_Sent_Token_Annotator() creates “simple” sentence token annotators. Argument f should be a sentence tokenizer, which takes a string s with the whole text to be processed, and returns the spans of the sentences in s, or an annotation object with these spans and (possibly) additional features. The generated annotator inherits from the default classes "Simple_Sent_Token_Annotator" and "Annotator". It uses the results of the simple sentence tokenizer to create and return annotations with unique ids and type ‘sentence’, possibly combined with sentence constituent features for already available paragraph annotations.

Simple_Word_Token_Annotator() creates “simple” word token annotators. Argument f should be a simple word tokenizer, which takes a string s giving a sentence to be processed, and returns the spans of the word tokens in s, or an annotation object with these spans and (possibly) additional features. The generated annotator inherits from the default classes "Simple_Word_Token_Annotator" and "Annotator". It uses already available sentence token annotations to extract the sentences and obtains the results of the word tokenizer for these. It then adds the sentence character offsets and unique word token ids, and word token constituents features for the sentences, and returns the word token annotations combined with the augmented sentence token annotations.

Simple_POS_Tag_Annotator() creates “simple” POS tag annotators. Argument f should be a simple POS tagger, which takes a character vector giving the word tokens in a sentence, and returns either a character vector with the tags, or a list of feature maps with the tags as ‘POS’ feature and possibly other features. The generated annotator inherits from the default classes "Simple_POS_Tag_Annotator" and "Annotator". It uses already available sentence and word token annotations to extract the word tokens for each sentence and obtains the results of the simple POS tagger for these, and returns annotations for the word tokens with the features obtained from the POS tagger.

Simple_Entity_Annotator() creates “simple” entity annotators. Argument f should be a simple entity detector (“named entity recognizer”) which takes a character vector giving the word tokens in a sentence, and return an annotation object with the word token spans, a ‘kind’ feature giving the kind of the entity detected, and possibly other features. The generated annotator inherits from the default classes "Simple_Entity_Annotator" and "Annotator". It uses already available sentence and word token annotations to extract the word tokens for each sentence and obtains the results of the simple entity detector for these, transforms word token spans to character spans and adds unique ids, and returns the combined entity annotations.

Simple_Chunk_Annotator() creates “simple” chunk annotators. Argument f should be a simple chunker, which takes as arguments character vectors giving the word tokens and the corresponding POS tags, and returns either a character vector with the chunk tags, or a list of feature lists with the tags as ‘chunk_tag’ feature and possibly other features. The generated annotator inherits from the default classes "Simple_Chunk_Annotator" and "Annotator". It uses already available annotations to extract the word tokens and POS tags for each sentence and obtains the results of the simple chunker for these, and returns word token annotations with the chunk features (only).

Simple_Stem_Annotator() creates “simple” stem annotators. Argument f should be a simple stemmer, which takes as arguments a character vector giving the word tokens, and returns a character vector with the corresponding word stems. The generated annotator inherits from the default classes "Simple_Stem_Annotator" and "Annotator". It uses already available annotations to extract the word tokens, and returns word token annotations with the corresponding stem features (only).

In all cases, if the underlying simple processing function returns annotation objects these should not provide their own ids (or use such in the features), as the generated annotators will necessarily provide these (the already available annotations are only available at the annotator level, but not at the simple processing level).

Value

An annotator object inheriting from the given classes and the default ones.

See Also

Package openNLP which provides annotator generators for sentence and word tokens, POS tags, entities and chunks, using processing functions based on the respective Apache OpenNLP MaxEnt processing resources.

Examples

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

## A very trivial sentence tokenizer.
sent_tokenizer <-
function(s) {
    s <- as.String(s)
    m <- gregexpr("[^[:space:]][^.]*\\.", s)[[1L]]
    Span(m, m + attr(m, "match.length") - 1L)
}
## (Could also use Regexp_Tokenizer() with the above regexp pattern.)
sent_tokenizer(s)
## A simple sentence token annotator based on the sentence tokenizer.
sent_token_annotator <- Simple_Sent_Token_Annotator(sent_tokenizer)
sent_token_annotator
a1 <- annotate(s, sent_token_annotator)
a1
## Extract the sentence tokens.
s[a1]

## A very trivial word tokenizer.
word_tokenizer <-
function(s) {
    s <- as.String(s)
    ## Remove the last character (should be a period when using
    ## sentences determined with the trivial sentence tokenizer).
    s <- substring(s, 1L, nchar(s) - 1L)
    ## Split on whitespace separators.
    m <- gregexpr("[^[:space:]]+", s)[[1L]]
    Span(m, m + attr(m, "match.length") - 1L)
}
lapply(s[a1], word_tokenizer)
## A simple word token annotator based on the word tokenizer.
word_token_annotator <- Simple_Word_Token_Annotator(word_tokenizer)
word_token_annotator
a2 <- annotate(s, word_token_annotator, a1)
a2
## Extract the word tokens.
s[subset(a2, type == "word")]

## A simple word token annotator based on wordpunct_tokenizer():
word_token_annotator <-
    Simple_Word_Token_Annotator(wordpunct_tokenizer,
                                list(description =
                                     "Based on wordpunct_tokenizer()."))
word_token_annotator
a2 <- annotate(s, word_token_annotator, a1)
a2
## Extract the word tokens.
s[subset(a2, type == "word")]

---

CoNLL-Style Text Documents

Description

Create text documents from CoNLL-style files.

Usage

CoNLLTextDocument(con, encoding = "unknown", format = "conll00",
                  meta = list())

Arguments

con	a connection object or a character string. See scan() for details.
encoding	encoding to be assumed for input strings. See scan() for details.
format	a character vector specifying the format. See Details.
meta	a named or empty list of document metadata tag-value pairs.

Details

CoNLL-style files use an extended tabular format where empty lines separate sentences, and non-empty lines consist of whitespace separated columns giving the word tokens and annotations for these. Such formats were popularized through their use for the shared tasks of CoNLL (Conference on Natural Language Learning), the yearly meeting of the Special Interest Group on Natural Language Learning of the Association for Computational Linguistics (see https://www.signll.org/content/conll/ for more information about CoNLL).

The precise format can vary according to corpus, and must be specified via argument format, as either a character string giving a pre-defined format, or otherwise a character vector with elements giving the names of the ‘fields’ (columns), and names used to give the field ‘types’, with ‘WORD’, ‘POS’ and ‘CHUNK’ to be used for, respectively, word tokens, POS tags, and chunk tags. For example,

  c(WORD = "WORD", POS = "POS", CHUNK = "CHUNK")

would be a format specification appropriate for the CoNLL-2000 chunking task, as also available as the pre-defined "conll00", which serves as default format for reasons of back-compatibility. Other pre-defined formats are "conll01" (for the CoNLL-2001 clause identification task), "conll02" (for the CoNLL-2002 language-independent named entity recognition task), "conllx" (for the CoNLL-X format used in at least the CoNLL-2006 and CoNLL-2007 multilingual dependency parsing tasks), and "conll09" (for the CoNLL-2009 shared task on syntactic and semantic dependencies in multiple languages).

The lines are read from the given connection and split into fields using scan(). From this, a suitable representation of the provided information is obtained, and returned as a CoNLL text document object inheriting from classes "CoNLLTextDocument" and "TextDocument".

There are methods for class "CoNLLTextDocument" and generics words(), sents(), tagged_words(), tagged_sents(), and chunked_sents() (as well as as.character()), which should be used to access the text in such text document objects.

The methods for generics tagged_words() and tagged_sents() provide a mechanism for mapping POS tags via the map argument, see section Details in the help page for tagged_words() for more information. The POS tagset used will be inferred from the POS_tagset metadata element of the CoNLL-style text document.

Value

An object inheriting from "CoNLLTextDocument" and "TextDocument".

See Also

TextDocument for basic information on the text document infrastructure employed by package NLP.

https://www.clips.uantwerpen.be/conll2000/chunking/ for the CoNLL-2000 chunking task, and training and test data sets which can be read in using CoNLLTextDocument().

---

CoNNL-U Text Documents

Description

Create text documents from CoNNL-U format files.

Usage

CoNLLUTextDocument(con, meta = list(), text = NULL)
read_CoNNLU(con)

Arguments

con	a connection object or a character string. See scan() for details.
meta	a named or empty list of document metadata tag-value pairs.
text	a character vector giving the text of the CoNNL-U annotation. If NULL, the text comments of the annotation are used.

Details

The CoNLL-U format (see https://universaldependencies.org/format.html) is a CoNLL-style format for annotated texts popularized and employed by the Universal Dependencies project (see https://universaldependencies.org/). For each “word” in the text, this provides exactly the 10 fields ID, FORM (word form or punctuation symbol), LEMMA (lemma or stem of word form), UPOSTAG (universal part-of-speech tag, see https://universaldependencies.org/u/pos/index.html), XPOSTAG (language-specific part-of-speech tag, may be unavailable), FEATS (list of morphological features), HEAD, DEPREL, DEPS, and MISC.

read_CoNNLU() reads the lines with these fields and optional comments from the given connection and splits into fields using scan(). This is combined with consecutive sentence ids into a data frame inheriting from class "CoNNLU_Annotation" used for representing the annotation information,

CoNLLUTextDocument() combines this annotation information with the given metadata (and optionally the original pre-tokenized text) into a CoNLL-U text document inheriting from classes "CoNLLUTextDocument" and "TextDocument".

The complete annotation information data frame can be extracted via content(). CoNLL-U v2 requires providing the complete texts of each sentence (or a reconstruction thereof) in ‘⁠# text =⁠’ comment lines. Where consistently provided, these are made available in the text attribute of the content data frame.

In addition, there are methods for generics as.character(), words(), sents(), tagged_words(), and tagged_sents() and class "CoNLLUTextDocument", which should be used to access the text in such text document objects.

The CoNLL-U format allows to represent both words and (multiword) tokens (see section ‘Words, Tokens and Empty Nodes’ in the format documentation), as distinguished by ids being integers or integer ranges, with the words being annotated further. One can use as.character() to extract the tokens; all other viewers listed above use the words. Finally, the viewers incorporating POS tags take a which argument to specify using the universal or language-specific tags, by giving a substring of "UPOSTAG" (default) or "XPOSTAG".

Value

For CoNLLUTextDocument(), an object inheriting from "CoNLLUTextDocument" and "TextDocument".

For read_CoNNLU(), an object inherting from "CoNNLU_Annotation" and "data.frame"

See Also

TextDocument for basic information on the text document infrastructure employed by package NLP.

https://universaldependencies.org/ for access to the Universal Dependencies treebanks, which provide annotated texts in many different languages using CoNLL-U format.

---

Parse ISO 8601 Date/Time Strings

Description

Extract date/time components from strings following one of the six formats specified in the NOTE-datetime ISO 8601 profile (https://www.w3.org/TR/NOTE-datetime).

Arguments

x	a character vector.

Details

For character strings in one of the formats in the profile, the corresponding date/time components are extracted, with seconds and decimal fractions of seconds combined. Other (malformed) strings are warned about.

The extracted components for each string are gathered into a named list with elements of the appropriate type (integer for year to min; double for sec; character for the time zone designator). The object returned is a (suitably classed) list of such named lists. This internal representation may change in future versions.

One can subscript such ISO 8601 date/time objects using [ and extract components using $ (where missing components will result in NAs), and convert them to the standard R date/time classes using as.Date(), as.POSIXct() and as.POSIXlt() (incomplete elements will convert to suitably missing elements). In addition, there are print() and as.data.frame() methods for such objects.

Value

An object inheriting from class "ISO_8601_datetime" with the extracted date/time components.

Examples

## Use the examples from <https://www.w3.org/TR/NOTE-datetime>, plus one
## in UTC.
x <- c("1997",
       "1997-07",
       "1997-07-16",
       "1997-07-16T19:20+01:00",
       "1997-07-16T19:20:30+01:00",
       "1997-07-16T19:20:30.45+01:00",
       "1997-07-16T19:20:30.45Z")
y <- parse_ISO_8601_datetime(x)
y
## Conversions: note that "incomplete" elements are converted to
## "missing".
as.Date(y)
as.POSIXlt(y)
## Subscripting and extracting components:
head(y, 3)
y$mon

---

Extract Annotation Features

Description

Conveniently extract features from annotations and annotated plain text documents.

Usage

features(x, type = NULL, simplify = TRUE)

Arguments

x	an object inheriting from class "Annotation" or "AnnotatedPlainTextDocument".
type	a character vector of annotation types to be used for selecting annotations, or NULL (default) to use all annotations. When selecting, the elements of type will partially be matched against the annotation types.
simplify	a logical indicating whether to simplify feature values to a vector.

Details

features() conveniently gathers all feature tag-value pairs in the selected annotations into a data frame with variables the values for all tags found (using a NULL value for tags without a value). In general, variables will be lists of extracted values. By default, variables where all elements are length one atomic vectors are simplified into an atomic vector of values. The values for specific tags can be extracted by suitably subscripting the obtained data frame.

Examples

## Use a pre-built annotated plain text document,
## see ? AnnotatedPlainTextDocument.
d <- readRDS(system.file("texts", "stanford.rds", package = "NLP"))
## Extract features of all *word* annotations in doc:
x <- features(d, "word")
## Could also have abbreviated "word" to "w".
x
## Only lemmas:
x$lemma
## Words together with lemmas:
paste(words(d), x$lemma, sep = "/")

---

Access or Modify Content or Metadata

Description

Access or modify the content or metadata of R objects.

Usage

content(x)
content(x) <- value
meta(x, tag = NULL, ...)
meta(x, tag = NULL, ...) <- value

Arguments

x	an R object.
value	a suitable R object.
tag	a character string or NULL (default), indicating to return the single metadata value for the given tag, or all metadata tag/value pairs.
...	arguments to be passed to or from methods.

Details

These are generic functions, with no default methods.

Often, classed R objects (e.g., those representing text documents in packages NLP and tm) contain information that can be grouped into “content”, metadata and other components, where content can be arbitrary, and metadata are collections of tag/value pairs represented as named or empty lists. The content() and meta() getters and setters aim at providing a consistent high-level interface to the respective information (abstracting from how classes internally represent the information).

Value

Methods for meta() should return a named or empty list of tag/value pairs if no tag is given (default), or the value for the given tag.

See Also

TextDocument for basic information on the text document infrastructure employed by package NLP.

---

Parse IETF Language Tag

Description

Extract language, script, region and variant subtags from IETF language tags.

Usage

parse_IETF_language_tag(x, expand = FALSE, strict = TRUE)

Arguments

x	a character vector with IETF language tags.
expand	a logical indicating whether to expand subtags into their description(s).
strict	a logical indicating whether invalid language tags should result in an error (default) or not.

Details

Internet Engineering Task Force (IETF) language tags are defined by IETF BCP 47, which is currently composed by the normative RFC 5646 and RFC 4647, along with the normative content of the IANA Language Subtag Registry regulated by these RFCs. These tags are used in a number of modern computing standards.

Each language tag is composed of one or more “subtags” separated by hyphens. Normal language tags have the following subtags:

• a language subtag (optionally, with language extension subtags),

• an optional script subtag,

• an optional region subtag,

• optional variant subtags,

• optional extension subtags,

• an optional private use subtag.

Language subtags are mainly derived from ISO 639-1 and ISO 639-2, script subtags from ISO 15924, and region subtags from ISO 3166-1 alpha-2 and UN M.49, see package ISOcodes for more information about these standards. Variant subtags are not derived from any standard. The Language Subtag Registry (https://www.iana.org/assignments/language-subtag-registry), maintained by the Internet Assigned Numbers Authority (IANA), lists the current valid public subtags, as well as the so-called “grandfathered” language tags.

See https://en.wikipedia.org/wiki/IETF_language_tag for more information.

Value

If expand is false, a list of character vectors of the form "type=subtag", where type gives the type of the corresponding subtag (one of ‘Language’, ‘Extlang’, ‘Script’, ‘Region’, ‘Variant’, or ‘Extension’), or "type=tag" with type either ‘Privateuse’ or ‘Grandfathered’.

Otherwise, a list of lists of character vectors obtained by replacing the subtags by their corresponding descriptions (which may be multiple) from the IANA registry. Note that no such descriptions for Extension and Privateuse subtags are available in the registry; on the other hand, empty expansions of the other subtags indicate malformed tags (as these subtags must be available in the registry).

Examples

## German as used in Switzerland:
parse_IETF_language_tag("de-CH")
## Serbian written using Latin script as used in Serbia and Montenegro:
parse_IETF_language_tag("sr-Latn-CS")
## Spanish appropriate to the UN Latin American and Caribbean region:
parse_IETF_language_tag("es-419")
## All in one:
parse_IETF_language_tag(c("de-CH", "sr-Latn-CS", "es-419"))
parse_IETF_language_tag(c("de-CH", "sr-Latn-CS", "es-419"),
                        expand = TRUE)
## Two grandfathered tags:
parse_IETF_language_tag(c("i-klingon", "zh-min-nan"),
                        expand = TRUE)

---

Compute N-Grams

Description

Compute the $n$-grams (contiguous sub-sequences of length $n$) of a given sequence.

Arguments

x	a sequence (vector).
n	a positive integer giving the length of contiguous sub-sequences to be computed.

Value

a list with the computed sub-sequences.

Examples

s <- "The quick brown fox jumps over the lazy dog"
## Split into words:
w <- strsplit(s, " ", fixed = TRUE)[[1L]]
## Word tri-grams:
ngrams(w, 3L)
## Word tri-grams pasted together:
vapply(ngrams(w, 3L), paste, "", collapse = " ")

---

Span objects

Description

Creation and manipulation of span objects.

Usage

Span(start, end)
as.Span(x)
is.Span(x)

Arguments

start, end	integer vectors giving the start and end positions of the spans.
x	an R object.

Details

A single span is a pair with “slots” ‘start’ and ‘end’, giving the start and end positions of the span.

Span objects provide sequences (allowing positional access) of single spans. They have class "Span". Span objects can be coerced to annotation objects via as.Annotation() (which of course is only appropriate provided that the spans are character spans of the natural language text being annotated), and annotation objects can be coerced to span objects via as.Span() (giving the character spans of the annotations).

Subscripting span objects via [ extracts subsets of spans; subscripting via $ extracts integer vectors with the sequence of values of the named slot.

There are several additional methods for class "Span": print() and format(); c() combines spans (or objects coercible to these using as.Span()), and as.list() and as.data.frame() coerce, respectively, to lists (of single span objects) and data frames (with spans and slots corresponding to rows and columns). Finally, one can add a scalar and a span object (resulting in shifting the start and end positions by the scalar).

Span() creates span objects from the given sequences of start and end positions, which must have the same length.

as.Span() coerces to span objects, with a method for annotation objects.

is.Span() tests whether an object inherits from class "Span" (and hence returns TRUE for both span and annotation objects).

Value

For Span() and as.Span(), a span object (of class "Span").

For is.Span(), a logical.

---

String objects

Description

Creation and manipulation of string objects.

Usage

String(x)
as.String(x)
is.String(x)

Arguments

x	a character vector with the appropriate encoding information for String(); an arbitrary R object otherwise.

Details

String objects provide character strings encoded in UTF-8 with class "String", which currently has a useful [ subscript method: with indices i and j of length one, this gives a string object with the substring starting at the position given by i and ending at the position given by j; subscripting with a single index which is an object inheriting from class "Span" or a list of such objects returns a character vector of substrings with the respective spans, or a list thereof.

Additional methods may be added in the future.

String() creates a string object from a given character vector, taking the first element of the vector and converting it to UTF-8 encoding.

as.String() is a generic function to coerce to a string object. The default method calls String() on the result of converting to character and concatenating into a single string with the elements separated by newlines.

is.String() tests whether an object inherits from class "String".

Value

For String() and as.String(), a string object (of class "String").

For is.String(), a logical.

Examples

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

## Basic sentence and word token annotation for the text.
a <- c(Annotation(1 : 2,
                  rep.int("sentence", 2L),
                  c( 3L, 20L),
                  c(17L, 35L)),
       Annotation(3 : 6,
                  rep.int("word", 4L),
                  c( 3L,  9L, 20L, 27L),
                  c( 7L, 16L, 25L, 34L)))

## All word tokens (by subscripting with an annotation object):
s[a[a$type == "word"]]
## Word tokens according to sentence (by subscripting with a list of
## annotation objects):
s[annotations_in_spans(a[a$type == "word"], a[a$type == "sentence"])]

---

Tagged_Token objects

Description

Creation and manipulation of tagged token objects.

Usage

Tagged_Token(token, tag)
as.Tagged_Token(x)
is.Tagged_Token(x)

Arguments

token, tag	character vectors giving tokens and the corresponding tags.
x	an R object.

Details

A tagged token is a pair with “slots” ‘token’ and ‘tag’, giving the token and the corresponding tag.

Tagged token objects provide sequences (allowing positional access) of single tagged tokens. They have class "Tagged_Token".

Subscripting tagged token objects via [ extracts subsets of tagged tokens; subscripting via $ extracts character vectors with the sequence of values of the named slot.

There are several additional methods for class "Tagged_Token": print() and format() (which concatenate tokens and tags separated by ‘⁠/⁠’); c() combines tagged token objects (or objects coercible to these using as.Tagged_Token()), and as.list() and as.data.frame() coerce, respectively, to lists (of single tagged token objects) and data frames (with tagged tokens and slots corresponding to rows and columns).

Tagged_Token() creates tagged token objects from the given sequences of tokens and tags, which must have the same length.

as.Tagged_Token() coerces to tagged token objects, with a method for TextDocument objects using tagged_words().

is.Tagged_Token() tests whether an object inherits from class "Tagged_Token".

Value

For Tagged_Token() and as.Tagged_Token(), a tagged token object (of class "Tagged_Token").

For is.Tagged_Token(), a logical.

---

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("\n", invert = TRUE),
                   para_tokenizer = blankline_tokenizer,
                   sep = "/",
                   meta = list())

Arguments

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.

Details

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.

The methods for generics tagged_words(), tagged_sents() and tagged_paras() provide a mechanism for mapping POS tags via the map argument, see section Details in the help page for tagged_words() for more information. The POS tagset used will be inferred from the POS_tagset metadata element of the CoNLL-style text document.

Value

A tagged text document object inheriting from "TaggedTextDocument" and "TextDocument".

See Also

https://www.nltk.org/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 TaggedTextDocument().

Package tm.corpus.Brown available from the repository at https://datacube.wu.ac.at conveniently provides this corpus as a tm VCorpus of tagged text documents.

---

NLP Tag Sets

Description

Tag sets frequently used in Natural Language Processing.

Usage

Penn_Treebank_POS_tags
Brown_POS_tags
Universal_POS_tags
Universal_POS_tags_map

Details

Penn_Treebank_POS_tags and Brown_POS_tags provide, respectively, the Penn Treebank POS tags (https://catalog.ldc.upenn.edu/docs/LDC95T7/cl93.html, Table 2) and the POS tags used for the Brown corpus (https://en.wikipedia.org/wiki/Brown_Corpus), both as data frames with the following variables:

entry

a character vector with the POS tags

description

a character vector with short descriptions of the tags

examples

a character vector with examples for the tags

Universal_POS_tags provides the universal POS tagset introduced by Slav Petrov, Dipanjan Das, and Ryan McDonald (doi:10.48550/arXiv.1104.2086), as a data frame with character variables entry and description.

Universal_POS_tags_map is a named list of mappings from language and treebank specific POS tagsets to the universal POS tags, with elements named ‘⁠en-ptb⁠’ and ‘⁠en-brown⁠’ giving the mappings, respectively, for the Penn Treebank and Brown POS tags.

Source

https://catalog.ldc.upenn.edu/docs/LDC95T7/cl93.html, http://www.nltk.org/nltk_data/, https://github.com/slavpetrov/universal-pos-tags.

Examples

## Penn Treebank POS tags
dim(Penn_Treebank_POS_tags)
## Inspect first 20 entries:
write.dcf(head(Penn_Treebank_POS_tags, 20L))

## Brown POS tags
dim(Brown_POS_tags)
## Inspect first 20 entries:
write.dcf(head(Brown_POS_tags, 20L))

## Universal POS tags
Universal_POS_tags

## Available mappings to universal POS tags
names(Universal_POS_tags_map)

---

Text Documents

Description

Representing and computing on text documents.

Details

Text documents are documents containing (natural language) text. In packages which employ the infrastructure provided by package NLP, such documents are represented via the virtual S3 class "TextDocument": such packages then provide S3 text document classes extending the virtual base class (such as the AnnotatedPlainTextDocument objects provided by package NLP itself).

All extension classes must provide an as.character() method which extracts the natural language text in documents of the respective classes in a “suitable” (not necessarily structured) form, as well as content() and meta() methods for accessing the (possibly raw) document content and metadata.

In addition, the infrastructure features the generic functions words(), sents(), etc., for which extension classes can provide methods giving a structured view of the text contained in documents of these classes (returning, e.g., a character vector with the word tokens in these documents, and a list of such character vectors).

See Also

AnnotatedPlainTextDocument, CoNLLTextDocument, CoNLLUTextDocument, TaggedTextDocument, and WordListDocument for the text document classes provided by package NLP.

---

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)]

---

Regexp tokenizers

Description

Tokenizers using regular expressions to match either tokens or separators between tokens.

Usage

Regexp_Tokenizer(pattern, invert = FALSE, ..., meta = list())
blankline_tokenizer(s)
whitespace_tokenizer(s)
wordpunct_tokenizer(s)

Arguments

pattern	a character string giving the regular expression to use for matching.
invert	a logical indicating whether to match separators between tokens.
...	further arguments to be passed to gregexpr().
meta	a named or empty list of tokenizer metadata tag-value pairs.
s	a String object, or something coercible to this using as.String() (e.g., a character string with appropriate encoding information).

Details

Regexp_Tokenizer() creates regexp span tokenizers which use the given pattern and ... arguments to match tokens or separators between tokens via gregexpr(), and then transform the results of this into character spans of the tokens found.

whitespace_tokenizer() tokenizes by treating any sequence of whitespace characters as a separator.

blankline_tokenizer() tokenizes by treating any sequence of blank lines as a separator.

wordpunct_tokenizer() tokenizes by matching sequences of alphabetic characters and sequences of (non-whitespace) non-alphabetic characters.

Value

Regexp_Tokenizer() returns the created regexp span tokenizer.

blankline_tokenizer(), whitespace_tokenizer() and wordpunct_tokenizer() return the spans of the tokens found in s.

See Also

Span_Tokenizer() for general information on span tokenizer objects.

Examples

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

spans <- whitespace_tokenizer(s)
spans
s[spans]

spans <- wordpunct_tokenizer(s)
spans
s[spans]

---

Tree objects

Description

Creation and manipulation of tree objects.

Usage

Tree(value, children = list())
## S3 method for class 'Tree'
format(x, width = 0.9 * getOption("width"), indent = 0,
       brackets = c("(", ")"), ...)
Tree_parse(x, brackets = c("(", ")"))
Tree_apply(x, f, recursive = FALSE)

Arguments

value	a (non-tree) node value of the tree.
children	a list giving the children of the tree.
x	a tree object for the format() method and Tree_apply(); a character string for Tree_parse().
width	a positive integer giving the target column for a single-line nested bracketting.
indent	a non-negative integer giving the indentation used for formatting.
brackets	a character vector of length two giving the pair of opening and closing brackets to be employed for formatting or parsing.
...	further arguments passed to or from other methods.
f	a function to be applied to the children nodes.
recursive	a logical indicating whether to apply f recursively to the children of the children and so forth.

Details

Trees give hierarchical groupings of leaves and subtrees, starting from the root node of the tree. In natural language processing, the syntactic structure of sentences is typically represented by parse trees (e.g., https://en.wikipedia.org/wiki/Concrete_syntax_tree) and displayed using nested brackettings.

The tree objects in package NLP are patterned after the ones in NLTK (https://www.nltk.org), and primarily designed for representing parse trees. A tree object consists of the value of the root node and its children as a list of leaves and subtrees, where the leaves are elements with arbitrary non-tree values (and not subtrees with no children). The value and children can be extracted via $ subscripting using names value and children, respectively.

There is a format() method for tree objects: this first tries a nested bracketting in a single line of the given width, and if this is not possible, produces a nested indented bracketting. The print() method uses the format() method, and hence its arguments to control the formatting.

Tree_parse() reads nested brackettings into a tree object.

Examples

x <- Tree(1, list(2, Tree(3, list(4)), 5))
format(x)
x$value
x$children

p <- Tree("VP",
          list(Tree("V",
                    list("saw")),
               Tree("NP",
                    list("him"))))
p <- Tree("S",
          list(Tree("NP",
                    list("I")),
               p))
p
## Force nested indented bracketting:
print(p, width = 10)

s <- "(S (NP I) (VP (V saw) (NP him)))"
p <- Tree_parse(s)
p

## Extract the leaves by recursively traversing the children and
## recording the non-tree ones:
Tree_leaf_gatherer <-
function()
{
    v <- list()
    list(update =
         function(e) if(!inherits(e, "Tree")) v <<- c(v, list(e)),
         value = function() v,
         reset = function() { v <<- list() })
}
g <- Tree_leaf_gatherer()
y <- Tree_apply(p, g$update, recursive = TRUE)
g$value()

---

Annotation Utilities

Description

Utilities for creating annotation objects.

Usage

next_id(id)
single_feature(value, tag)

Arguments

id	an integer vector of annotation ids.
value	an R object.
tag	a character string.

Details

next_id() obtains the next “available” id based on the given annotation ids (one more than the maximal non-missing id).

single_feature() creates a single feature from the given value and tag (i.e., a named list with the value named by the tag).

---

Text Document Viewers

Description

Provide suitable “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

x	a text document object.
...	further arguments to be passed to or from methods.

Details

Methods for extracting POS tagged word tokens (i.e., for generics tagged_words(), tagged_sents() and tagged_paras()) can optionally provide a mechanism for mapping the POS tags via a 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 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 dámelo = da me lo. For these, words() gives the syntactic word tokens, and otoks() the orthographic word tokens. This is currently supported for CoNNL-U text documents (see https://universaldependencies.org/format.html for more information) and annotated plain text documents (via word features as used for example for some Stanford CoreNLP annotator pipelines provided by package StanfordCoreNLP available from the repository at https://datacube.wu.ac.at).

In addition to methods for the text document classes provided by package NLP itself, (see TextDocument), package NLP also provides word tokens and POS tagged word tokens for the results of udpipe_annotate() from package udpipe, spacy_parse() from package spacyr, and cnlp_annotate() from package cleanNLP.

Value

For words(), a character vector with the word tokens in the document.

For sents(), a list of character vectors with the word tokens in the sentences.

For paras(), a list of lists of character vectors with the word tokens in the sentences, grouped according to the paragraphs.

For 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 ‘⁠/⁠’).

For tagged_sents(), a list of character vectors with the POS tagged word tokens in the sentences.

For tagged_paras(), a list of lists of character vectors with the POS tagged word tokens in the sentences, grouped according to the paragraphs.

For chunked_sents(), a list of (flat) Tree objects giving the chunk trees for the sentences in the document.

For parsed_sents(), a list of Tree objects giving the parse trees for the sentences in the document.

For parsed_paras(), a list of lists of Tree objects giving the parse trees for the sentences in the document, grouped according to the paragraphs in the document.

For otoks(), a character vector with the orthographic word tokens in the document.

See Also

TextDocument for basic information on the text document infrastructure employed by package 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)

---

Word List Text Documents

Description

Create text documents from word lists.

Usage

WordListDocument(con, encoding = "unknown", meta = list())

Arguments

con	a connection object or a character string. See readLines() for details.
encoding	encoding to be assumed for input strings. See readLines() for details.
meta	a named or empty list of document metadata tag-value pairs.

Details

WordListDocument() uses readLines() to read collections of words from connections for which each line provides one word, with blank lines ignored, and returns a word list document object which inherits from classes "WordListDocument" and "TextDocument".

The methods for generics words() and as.character() and class "WordListDocument" can be used to extract the words.

Value

A word list document object inheriting from "WordListDocument" and "TextDocument".

See Also

TextDocument for basic information on the text document infrastructure employed by package NLP.

---