Maxent_Chunk_Annotator: Apache OpenNLP based chunk annotators

Description Usage Arguments Details Value See Also Examples

View source: R/chunk.R

Description

Generate an annotator which computes chunk annotations using the Apache OpenNLP Maxent chunker.

Usage

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Maxent_Chunk_Annotator(language = "en", probs = FALSE, model = NULL)

Arguments

language

a character string giving the ISO-639 code of the language being processed by the annotator.

probs

a logical indicating whether the computed annotations should provide the token probabilities obtained from the Maxent model as their ‘chunk_prob’ feature.

model

a character string giving the path to the Maxent model file to be used, or NULL indicating to use a default model file for the given language (if available, see Details).

Details

See http://opennlp.sourceforge.net/models-1.5/ for available model files. These can conveniently be made available to R by installing the respective openNLPmodels.language package from the repository at https://datacube.wu.ac.at.

Value

An Annotator object giving the generated chunk annotator.

See Also

https://opennlp.apache.org for more information about Apache OpenNLP.

Examples

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## Requires package 'openNLPmodels.en' from the repository at
## <https://datacube.wu.ac.at>.

require("NLP")
## Some text.
s <- paste(c("Pierre Vinken, 61 years old, will join the board as a ",
             "nonexecutive director Nov. 29.\n",
             "Mr. Vinken is chairman of Elsevier N.V., ",
             "the Dutch publishing group."),
           collapse = "")
s <- as.String(s)

## Chunking needs word token annotations with POS tags.
sent_token_annotator <- Maxent_Sent_Token_Annotator()
word_token_annotator <- Maxent_Word_Token_Annotator()
pos_tag_annotator <- Maxent_POS_Tag_Annotator()
a3 <- annotate(s,
               list(sent_token_annotator,
                    word_token_annotator,
                    pos_tag_annotator))

annotate(s, Maxent_Chunk_Annotator(), a3)
annotate(s, Maxent_Chunk_Annotator(probs = TRUE), a3)

openNLP documentation built on Oct. 30, 2019, 11:37 a.m.