In trinker/textstem: Tools for Stemming and Lemmatizing Text

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 <- ''

pacman::p_load(textstem)
pacman::p_load_current_gh('trinker/numform')
nr <- numform::f_comma(length(presidential_debates_2012$dialogue))
nw <- numform::f_comma(sum(stringi::stri_count_words(presidential_debates_2012$dialogue), na.rm = TRUE))
````

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[![](https://cranlogs.r-pkg.org/badges/textstem)](https://cran.r-project.org/package=textstem)
`r verbadge`

![](tools/textstem_logo/r_textstem.png)

**textstem** is a tool-set for stemming and lemmatizing words.  Stemming is a process that removes affixes.   Lemmatization is the process of grouping inflected forms together as a single base form.  

# Functions

The main functions, task category, & descriptions are summarized in the table below:


| Function                      | Task        | Description                                | 
|-------------------------------|-------------|--------------------------------------------| 
| `stem_words`                  | stemming    | Stem words                                 | 
| `stem_strings`                | stemming    | Stem strings                               | 
| `lemmatize_words`             | lemmatizing | Lemmatize words                            | 
| `lemmatize_strings`           | lemmatizing | Lemmatize strings                          | 
| `make_lemma_dictionary_words` | lemmatizing | Generate a dictionary of lemmas for a text |     


# Installation

To download the development version of **textstem**:

Download the [zip ball](https://github.com/trinker/textstem/zipball/master) or [tar ball](https://github.com/trinker/textstem/tarball/master), decompress and run `R CMD INSTALL` on it, or use the **pacman** package to install the development version:

```r
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/textstem")

Contact

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

Examples

The following examples demonstrate some of the functionality of textstem.

Load the Tools/Data

if (!require("pacman")) install.packages("pacman")
pacman::p_load(textstem, dplyr)

data(presidential_debates_2012)

Stemming Versus Lemmatizing

Before moving into the meat these two examples let's highlight the difference between stemming and lemmatizing.

"Drive" Stemming vs. Lemmatizing

dw <- c('driver', 'drive', 'drove', 'driven', 'drives', 'driving')

stem_words(dw)
lemmatize_words(dw)

"Be" Stemming vs. Lemmatizing

bw <- c('are', 'am', 'being', 'been', 'be')

stem_words(bw)
lemmatize_words(bw)

Stemming

Stemming is the act of removing inflections from a word not necessarily "identical to the morphological root of the word" (wikipedia). Below I show stemming of several small strings.

y <- c(
    'the dirtier dog has eaten the pies',
    'that shameful pooch is tricky and sneaky',
    "He opened and then reopened the food bag",
    'There are skies of blue and red roses too!',
    NA,
    "The doggies, well they aren't joyfully running.",
     "The daddies are coming over...",
    "This is 34.546 above"
)
stem_strings(y)

Lemmatizing

Default Lemma Dictionary

Lemmatizing is the "grouping together the inflected forms of a word so they can be analysed as a single item" (wikipedia). In the example below I reduce the strings to their lemma form. lemmatize_strings uses a lookup dictionary. The default uses Mechura's (2016) English lemmatization list available from the lexicon package. The make_lemma_dictionary function contains two additional engines for generating a lemma lookup table for use in lemmatize_strings.

y <- c(
    'the dirtier dog has eaten the pies',
    'that shameful pooch is tricky and sneaky',
    "He opened and then reopened the food bag",
    'There are skies of blue and red roses too!',
    NA,
    "The doggies, well they aren't joyfully running.",
     "The daddies are coming over...",
    "This is 34.546 above"
)
lemmatize_strings(y)

Hunspell Lemma Dictionary

This lemmatization uses the hunspell package to generate lemmas.

lemma_dictionary_hs <- make_lemma_dictionary(y, engine = 'hunspell')
lemmatize_strings(y, dictionary = lemma_dictionary_hs)

koRpus Lemma Dictionary

This lemmatization uses the koRpus package and the TreeTagger program to generate lemmas. You'll have to get TreeTagger set up, preferably in your machine's root directory.

lemma_dictionary_tt <- make_lemma_dictionary(y, engine = 'treetagger')
lemmatize_strings(y, lemma_dictionary_tt)

Lemmatization Speed

It's pretty fast too. Observe:

tic <- Sys.time()

presidential_debates_2012$dialogue %>%
    lemmatize_strings() %>%
    head()

(toc <- Sys.time() - tic)

That's r nr rows of text, or r nw words, in r round(as.numeric(toc), 2) seconds.

Combine With Other Text Tools

This example shows how stemming/lemmatizing might be complemented by other text tools such as replace_contraction from the textclean package.

library(textclean)

'aren\'t' %>% 
    lemmatize_strings()

'aren\'t' %>% 
    textclean::replace_contraction() %>%
    lemmatize_strings()

trinker/textstem documentation built on June 1, 2019, 1:47 a.m.