In trinker/parsent: Sentence Parsing Tools

desc <- suppressWarnings(readLines("DESCRIPTION"))
regex <- "(^Version:\\s+)(\\d+\\.\\d+\\.\\d+)"
loc <- grep(regex, desc)
ver <- gsub(regex, "\\2", desc[loc])
verbadge <- sprintf('<a href="https://img.shields.io/badge/Version-%s-orange.svg"><img src="https://img.shields.io/badge/Version-%s-orange.svg" alt="Version"/></a></p>', ver, ver)
verbadge <- ''
````

[![Build Status](https://travis-ci.org/trinker/parsent.svg?branch=master)](https://travis-ci.org/trinker/parsent)
[![Coverage Status](https://coveralls.io/repos/trinker/parsent/badge.svg?branch=master)](https://coveralls.io/r/trinker/parsent?branch=master)
`r verbadge`

```r
library(knitr)
knit_hooks$set(htmlcap = function(before, options, envir) {
  if(!before) {
    paste('<p class="caption"><b><em>',options$htmlcap,"</em></b></p>",sep="")
    }
    })
knitr::opts_knit$set(self.contained = TRUE, cache = FALSE)
knitr::opts_chunk$set(fig.path = "tools/figure/")

parsent is a collection of tools used to parse sentences. The package is a wrapper for the NLP/openNLP packages that simplifies and extends the user experience.

Function Usage

Functions typically fall into the task category of (1) parsing, (2) converting, & (3) extracting. The main functions, task category, & descriptions are summarized in the table below:

| Function | Task | Description | |---------------------------|------------|-----------------------------------------------------------| | parser | parsing | Parse sentences into phrases | | parse_annotator | parsing | Generate OpenNLP parser required by parser function | | as_tree | converting | Convert parser output into tree form | | as_square_brace | converting | Convert parser output in square brace form (vs. round) | | as_square_brace_latex | converting | Convert parser output LaTeX ready form | | get_phrases | extracting | Extract phrases from parser output | | get_phrase_type | extracting | Extract phrases one step down the tree | | get_phrase_type_regex | extracting | Extract phrases at any level in the tree (uses regex) | | get_leaves | extracting | Extract the leaves (tokens or words) from a phrase | | take | extracting | Select indexed elements from a vector |

Installation

To download the development version of parsent:

Download the zip ball or tar ball, decompress and run R CMD INSTALL on it, or use the pacman package to install the development version:

if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh(c(
    "trinker/textshape", 
    "trinker/coreNLPsetup",          
    "trinker/parsent"
))

Contact

You are welcome to: submit suggestions and bug-reports at: https://github.com/trinker/parsent/issues send a pull request on: https://github.com/trinker/parsent/ * compose a friendly e-mail to: [e-mail]

Demonstration

Load the Packages/Data

if (!require("pacman")) install.packages("pacman")

pacman::p_load(parsent, magrittr)

txt <- c(
    "Really, I like chocolate because it is good. It smells great.",
    "Robots are rather evil and most are devoid of decency.",
    "He is my friend.",
    "Clifford the big red dog ate my lunch.",
    "Professor Johns can not teach",
    "",
    NA
)

Create Annotator

if(!exists('parse_ann')) {
    parse_ann <- parse_annotator()
}

Parsing

(x <- parser(txt, parse.annotator = parse_ann))

Note that the user may choose to use CoreNLP as a backend by setting engine = "coreNLP". To ensure that coreNLP is setup properly use check_setup.

Plotting

par(mar = c(0,0,0,.7) + 0.2)
plot(x[[2]])

par(
    mfrow = c(3, 2),
    mar = c(0,0,1,1) + 0.1
)
invisible(lapply(x[1:5], plot))

Get Subject, Verb, and Direct Object

Subject

get_phrase_type(x, "NP") %>%
    take() %>%
    get_leaves()

Predicate Verb

get_phrase_type_regex(x, "VP") %>%
    take() %>%
    get_phrase_type_regex("(VB|MD)") %>%
    take() %>%
    get_leaves()

Direct Object

get_phrase_type_regex(x, "VP") %>%
    take() %>%
    get_phrase_type_regex("NP") %>%
    take() %>%
    get_leaves()

trinker/parsent documentation built on May 31, 2019, 9:41 p.m.