NLP (version 0.2-0)

Annotation: 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 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.

Value

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

For is.Annotation(), a logical.

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".

Examples

# NOT RUN {
## 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.
# }

Run the code above in your browser using DataCamp Workspace