R/TextExtraction.r

In jaredlander/TextInfo: Functions for extracting information from text

#' @title extractTextInfo
#' @description Given a vector of text, detect sentence-wise entity pairings.
#' @details Given a vector of text, detect sentence-wise entity pairings.  Works even if senetences are broken up over multiple elements of the vector of text.
#' @aliases extractTextInfo
#' @export extractTextInfo
#' @author Jared P. Lander
#' @importFrom magrittr "%>%"
#' @importFrom  dplyr group_by do
#' @importFrom useful "reclass<-"
#' @param text A vector of text
#' @param nerModel A ner model supplied by MITIE
#' @param requiredTerms A vector of terms that must be extracted if they exist
#' @param ignore.case Logical indicating if requiredTerms is not case sensitive
#' @return A tbl of entity pairs demarcated by the sentence they are paired in.  Could result in multiple combinations.
#' @examples 
#' \dontrun{
#' ner_model_path <- "tests/data/ner_model.dat"
#' ner <- NamedEntityExtractor$new(ner_model_path)
#' thisText <- c("A network of new super PACs said Wednesday that it had raised $31 million to support Senator Ted Cruz's presidential campaign, a sum that could upend expectations in the race for the Republican nomination and rewrite the political rule book for outside spending.",
#' "The groups, four super PACs sharing variations of the name Keep the Promise, were established and secured commitments with virtually no warning over the course of several days beginning Monday.",
#' "Dathan Voelter, an Austin, Tex., lawyer and friend of Mr. Cruz who is serving as treasurer for three of the super PACs, said the four organizations would operate in tandem, all seeking to help elect the Texas senator as president. Most of the contributions have already arrived, he said, and the remainder will be collected by the four groups by the end of the week.",
#' "The dollar figures could not be independently verified, and none of the groups will need to file campaign disclosures with the Federal Election Commission until July. But an outside spending campaign of that size, combined with Mr. Cruz's demonstrated ability to pull in dollars from small donors, would substantially offset Mr. Cruz's difficulties in building a traditional network of regular large donors and volunteer fund-raisers, known as bundlers.",
#' "The size of the contributions is likely to force backers of other candidates to rethink their budgets for the primary season; other super PACs lining up behind Republican candidates had planned to raise $20 million to $30 million over the course of the entire primary campaign.")
#' extractTextInfo(thisText, nerModel=ner)
#' extractTextInfo(thisText, nerModel=ner, requiredTerms=c('dollar'))
#' }
#' 
extractTextInfo <- function(text, nerModel, requiredTerms=NULL, ignore.case=TRUE)
{
    # for one file split into sentences
    sentences <- getSentencesFromText(text)
    
    # if nerModel is a path build the ner entity
    # if(is.character(nerModel))
    # {
    #     nerModel <- MITIE::NamedEntityExtractor$new(nerModel)
    # }
    
    # get paired entities
    sentences %>% 
        # for each sentence
        group_by(Number) %>% 
        # extract entities mentioned
        do(extractSentenceInfo(.$Sentence, nerModel=nerModel, requiredTerms=requiredTerms, ignore.case=ignore.case)) %>% 
        # group this by entity and get counts
        # group_by(Entity) %>% 
        # summarize(Count=n()) %>% 
        # ungroup %>% 
        # create edgelist of mentioned entities
        do(pairEntities(.)) %>%
        # make it a pairedEntity class
        reclass(value='pairedEntity')
}

#' @title getSentencesFromText
#' @description Given text, this extracts all the sentences based on \code{\link[qdaplite]{sent_detect}}.
#' @details Given text, this extracts all the sentences based on \code{\link[qdaplite]{sent_detect}}.
#' @export getSentencesFromText
#' @aliases getSentencesFromText
#' @author Jared P. Lander
#' @importFrom dplyr data_frame mutate n as.tbl
#' @importFrom magrittr "%>%"
#' @importFrom qdaplite sent_detect
#' @param text Text to be split into sentences
#' @return A data.frame with one row per sentence detected
#' @examples 
#' getSentencesFromText(c('This is one sentence in a single element.', 'This element will hold multiple sentences. This is the second. And this is the third and last.'))
#' 
getSentencesFromText <- function(text)
{
    # for one file split into sentences
    data_frame(Sentence=sent_detect(text)) %>% 
        # one row per sentence
        mutate(Number=1:n()) %>% 
        as.tbl
}