Teradata Package for R Function Reference | 17.00 - TextChunker - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

TextChunker

Description

The TextChunker function divides text into phrases and assigns each phrase a tag that identifies its type.

Usage

  td_text_chunker_mle (
      data = NULL,
      word.column = NULL,
      pos.column = NULL,
      data.sequence.column = NULL,
      data.partition.column = NULL,
      data.order.column = NULL
  )

Arguments

data	Required Argument. Specifies the tbl_teradata that contains the text to be scanned.
data.partition.column	Required Argument. Specifies Partition By columns for "data". Values to this argument can be provided as a vector, if multiple columns are used for partition. Types: character OR vector of Strings (character)
data.order.column	Required Argument. Specifies Order By columns for "data". Values to this argument can be provided as a vector, if multiple columns are used for ordering. Types: character OR vector of Strings (character)
word.column	Required Argument. Specifies the name of the input tbl_teradata column that contains the words to chunk into phrases. Typically, this is the word column of the output tbl_teradata of the POSTagger (td_pos_tagger_mle) function. Types: character
pos.column	Required Argument. Specifies the name of the input tbl_teradata column that conatins the part-of-speech (POS) tag of words. Typically, this is the pos_tag column of the output tbl_teradata of the td_pos_tagger_mle function. Types: character
data.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "data". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)

Value

Function returns an object of class "td_text_chunker_mle" which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator using name: result.

Examples

  
    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("text_chunker_example", "posttagger_output")
    
    # Create object(s) of class "tbl_teradata".
    posttagger_output <- tbl(con, "posttagger_output")
    
    # Example 1 - This example uses the persisted output of the td_pos_tagger_mle() function 
    # as input.
    text_chunker_out1 <- td_text_chunker_mle(data=posttagger_output,
                                data.partition.column='paraid',
                                data.order.column=c('paraid','word_sn'),
                                word.column='word',
                                pos.column='pos_tag',
                                data.sequence.column='paraid')
                                
    # Load example data.
    loadExampleData("pos_tagger_example", "paragraphs_input")
    
    # Create remote tibble objects.
    paragraphs_input <- tbl(con, "paragraphs_input")
    
    # Example 2 - This example uses output of the td_pos_tagger_mle() function as input. The output 
    # of the td_pos_tagger_mle() function is generated using the td_sentence_extractor_mle() 
    # function as input.
    td_sentence_extractor_out <- td_sentence_extractor_mle(data = paragraphs_input,
                                                         text.column = "paratext",
                                                         accumulate = "paraid")
  
    sentenceextractor_out <- td_sentence_extractor_out$result 
                             
  
    pos_tagger_out <- td_pos_tagger_mle(data=sentenceextractor_out,
                                      text.column='sentence',
                                      accumulate='sentence_sn')
  
    text_chunker_out2 <- td_text_chunker_mle(data=pos_tagger_out$result,
                                data.partition.column='word_sn',
                                data.order.column='word_sn',
                                word.column='word',
                                pos.column='pos_tag',
                                data.sequence.column='word_sn')