tokenize: Tokenize a character vector Parse the elements of a character...

In lexRankr: Extractive Summarization of Text with the LexRank Algorithm

Description

Tokenize a character vector Parse the elements of a character vector into a list of cleaned tokens.

Usage

tokenize(text, removePunc = TRUE, removeNum = TRUE, toLower = TRUE,
  stemWords = TRUE, rmStopWords = TRUE)

Arguments

text	The character vector to be tokenized
removePunc	TRUE or FALSE indicating whether or not to remove punctuation from text. If TRUE, punctuation will be removed. Defaults to TRUE.
removeNum	TRUE or FALSE indicating whether or not to remove numbers from text. If TRUE, numbers will be removed. Defaults to TRUE.
toLower	TRUE or FALSE indicating whether or not to coerce all of text to lowercase. If TRUE, text will be coerced to lowercase. Defaults to TRUE.
stemWords	TRUE or FALSE indicating whether or not to stem resulting tokens. If TRUE, the outputted tokens will be tokenized using SnowballC::wordStem(). Defaults to TRUE.
rmStopWords	TRUE, FALSE, or character vector of stopwords to remove. If TRUE, words in lexRankr::smart_stopwords will be removed prior to stemming. If FALSE, no stopword removal will occur. If a character vector is passed, this vector will be used as the list of stopwords to be removed. Defaults to TRUE.

Examples

tokenize("Mr. Feeny said the test would be on Sat. At least I'm 99.9% sure that's what he said.")
tokenize("Bill is trying to earn a Ph.D. in his field.", rmStopWords=FALSE)

Example output

[[1]]
[1] "mr"    "feeni" "test"  "sat"   "im"   

[[1]]
 [1] "bill"  "i"     "try"   "to"    "earn"  "a"     "phd"   "in"    "hi"   
[10] "field"

lexRankr documentation built on May 2, 2019, 1:29 p.m.