| Title: | Syllable Counting and Readability Measurements |
|---|---|
| Description: | An English language syllable counter, plus readability score measure-er. For readability, we support 'Flesch' Reading Ease and 'Flesch-Kincaid' Grade Level ('Kincaid' 'et al'. 1975) <https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1055&context=istlibrary>, Automated Readability Index ('Senter' and Smith 1967) <https://apps.dtic.mil/sti/citations/AD0667273>, Simple Measure of Gobbledygook (McLaughlin 1969), and 'Coleman-Liau' (Coleman and 'Liau' 1975) <doi:10.1037/h0076540>. The package has been carefully optimized and should be very efficient, both in terms of run time performance and memory consumption. The main methods are 'vectorized' by document, and scores for multiple documents are computed in parallel via 'OpenMP'. |
| Authors: | Drew Schmidt [aut, cre] |
| Maintainer: | Drew Schmidt <[email protected]> |
| License: | BSD 2-clause License + file LICENSE |
| Version: | 0.2-6 |
| Built: | 2024-11-18 06:34:33 UTC |
| Source: | CRAN |
An English language syllable counter, plus readability score measure-er. For readability, we support Flesch Reading Ease and Flesch-Kincaid Grade Level (Kincaid et al. 1975), Automated Readability Index (Senter and Smith 1967), Simple Measure of Gobbledygook (McLaughlin 1969), and Coleman-Liau (Coleman and Liau 1975). The package has been carefully optimized and should be very efficient, both in terms of run time performance and memory consumption. The main methods are vectorized by document, and scores for multiple documents are computed in parallel via OpenMP.
Drew Schmidt
Computes some basic document counts (see the 'Value' section below for details).
The function is vectorized by document, and scores are computed in parallel
via OpenMP. You can control the number of threads used with the
nthreads parameter.
doc_counts(s, nthreads = sylcount.nthreads())doc_counts(s, nthreads = sylcount.nthreads())
s |
A character vector (vector of strings). |
nthreads |
Number of threads to use. By default it will use the total number of cores + hyperthreads. |
The function is essentially just readability() without the readability
scores.
A dataframe containing:
chars |
the total numberof characters |
wordchars |
the number of alphanumeric characters |
words |
text tokens that are probably English language words |
nonwords |
text tokens that are probably not English language words |
sents |
the number of sentences recognized in the text |
sylls |
the total number of syllables (ignores all non-words) |
polys |
the total number of "polysyllables", or words with 3+ syllables |
library(sylcount) a <- "I am the very model of a modern major general." b <- "I have information vegetable, animal, and mineral." doc_counts(c(a, b), nthreads=1)library(sylcount) a <- "I am the very model of a modern major general." b <- "I have information vegetable, animal, and mineral." doc_counts(c(a, b), nthreads=1)
Computes some basic "readability" measurements, includeing Flesch Reading
Ease, Flesch-Kincaid grade level, Automatic Readability Index, and the
Simple Measure of Gobbledygook. The function is vectorized by document, and
scores are computed in parallel via OpenMP. You can control the number of
threads used with the nthreads parameter.
The function will have some difficulty on poorly processed and cleaned data. For example, if all punctuation is stripped out, then the number of sentences detected will always be zero. However, we do recommend removing quotes (single and double), as contractions can confuse the parser.
readability(s, nthreads = sylcount.nthreads())readability(s, nthreads = sylcount.nthreads())
s |
A character vector (vector of strings). |
nthreads |
Number of threads to use. By default it will use the total number of cores + hyperthreads. |
The return is split into words and non-words. A non-word is some block of text more than 64 characters long with no spaces or sentence-ending punctuation inbetween. The number of non-words is returned mostly for error-checking/debugging purposes. If you have a lot of non-words, you probably didn't clean your text properly. The word/non-word division is made in an attempt to improve run-time and memory performance.
For implementation details, see the Details section of ?sylcount.
A dataframe containing:
chars |
the total numberof characters |
wordchars |
the number of alphanumeric characters |
words |
text tokens that are probably English language words |
nonwords |
text tokens that are probably not English language words |
sents |
the number of sentences recognized in the text |
sylls |
the total number of syllables (ignores all non-words) |
polys |
the total number of "polysyllables", or words with 3+ syllables |
re |
Flesch reading ease score |
gl |
Flesch-Kincaid grade level score |
ari |
Automatic Readability Index score |
smog |
Simple Measure of Gobbledygook (SMOG) score |
cl |
the Coleman-Liau Index score |
Kincaid, J. Peter, et al. Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. No. RBR-8-75. Naval Technical Training Command Millington TN Research Branch, 1975.
Senter, R. J., and Edgar A. Smith. Automated readability index. CINCINNATI UNIV OH, 1967.
McLaughlin, G. Harry. "SMOG grading-a new readability formula." Journal of reading 12.8 (1969): 639-646.
Coleman, Meri, and Ta Lin Liau. "A computer readability formula designed for machine scoring." Journal of Applied Psychology 60.2 (1975): 283.
library(sylcount) a <- "I am the very model of a modern major general." b <- "I have information vegetable, animal, and mineral." # One or the other readability(a, nthreads=1) readability(b, nthreads=1) # Bot at once as separate documents. readability(c(a, b), nthreads=1) # And as a single document. readability(paste0(a, b, collapse=" "), nthreads=1)library(sylcount) a <- "I am the very model of a modern major general." b <- "I have information vegetable, animal, and mineral." # One or the other readability(a, nthreads=1) readability(b, nthreads=1) # Bot at once as separate documents. readability(c(a, b), nthreads=1) # And as a single document. readability(paste0(a, b, collapse=" "), nthreads=1)
A vectorized syllable counter for English language text.
Because of the R memory allocations required, the operation is not thread safe. It is evaluated in serial.
sylcount(s, counts.only = TRUE)sylcount(s, counts.only = TRUE)
s |
A character vector (vector of strings). |
counts.only |
Should only counts be returned, or words + counts? |
The maximum supported word length is 64 characters. For any token having more
than 64 characters, the returned syllable count will be NA.
The syllable counter uses a hash table of known, mostly "irregular" (with respect to syllable counting) words. If the word is not known to us (i.e., not in the hash table), then we try to "approximate" the number of syllables by counting the number of non-consecutive vowels in a word.
So for example, using this scheme, each of "to", "too", and "tool" would be classified as having one syllable. However, "tune" would be classified as having 2. Fortunately, "tune" is in our table, listed as having 1 syllable.
The hash table uses a perfect hash generated by gperf.
A list of dataframes.
library(sylcount) a <- "I am the very model of a modern major general." b <- "I have information vegetable, animal, and mineral." sylcount(c(a, b)) sylcount(c(a, b), counts.only=FALSE)library(sylcount) a <- "I am the very model of a modern major general." b <- "I have information vegetable, animal, and mineral." sylcount(c(a, b)) sylcount(c(a, b), counts.only=FALSE)
Returns the number of cores + hyperthreads on the system. The function
respects the environment variable OMP_NUM_THREADS.
sylcount.nthreads()sylcount.nthreads()
An integer; the number of threads.