| Title: | Mixed N-Grams and Unigram Sequence Segmentation |
|---|---|
| Description: | Segmentation of short text sequences - like hashtags - into the separated words sequence, done with the use of dictionary, which may be built on custom corpus of texts. Unigram dictionary is used to find most probable sequence, and n-grams approach is used to determine possible segmentation given the text corpus. |
| Authors: | Oskar Kosch [aut, cre] |
| Maintainer: | Oskar Kosch <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.0 |
| Built: | 2024-11-26 05:38:44 UTC |
| Source: | https://github.com/theogrost/nuss |
Data contains English unigrams, with their replacements, points and ids.
data(base_dictionary)data(base_dictionary)
A data.frame with four columns: to_search, to_replace, points, id.
Created based on Wikipedia unigrams.
data(base_dictionary)data(base_dictionary)
This function takes character vector patterns with regex patterns
(or fixed strings),
and searches for match in the x string. It is inverse in the meaning,
that in grepl single pattern is used against multiple strings;
instead, this function takes multiple patterns to fit on a single string.
igrepl(patterns, x, fixed = FALSE)igrepl(patterns, x, fixed = FALSE)
patterns |
a character vector of regex or fixed patterns. |
x |
a string to search for the match. |
fixed |
a logical, indicating whether patterns are fixed strings. |
Logical vector of length as patterns with true if pattern
was found.
igrepl(c("today","b.* fox", "jumps over", "vigorous"), "The quick brown fox jumps over the lazy dog", FALSE) igrepl(c("today","brown fox", "jumps over", "vigorous"), "The quick brown fox jumps over the lazy dog", TRUE)igrepl(c("today","b.* fox", "jumps over", "vigorous"), "The quick brown fox jumps over the lazy dog", FALSE) igrepl(c("today","brown fox", "jumps over", "vigorous"), "The quick brown fox jumps over the lazy dog", TRUE)
ngrams_dictionary returns the data.frame containing dictionary for
ngrams_segmentation.
ngrams_dictionary( texts, clean = TRUE, ngram_min = 1, ngram_max = 5, points_filter = 1 )ngrams_dictionary( texts, clean = TRUE, ngram_min = 1, ngram_max = 5, points_filter = 1 )
texts |
character vector, these are the texts used to create n-grams dictionary. Case-sensitive. |
clean |
logical, indicating if the texts should be cleaned before creating n-grams dictionary. |
ngram_min |
numeric, sets the minimum number of words in creating the dictionary. |
ngram_max |
numeric, sets the maximum number of words in creating the dictionary. |
points_filter |
numeric, sets the minimal number of points (occurrences) of an n-gram to be included in the dictionary. |
The output always will be data.frame with 4 columns: 1) to_search, 2) to_replace, 3) id, 4) points.
texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") ngrams_dictionary(texts) ngrams_dictionary(texts, clean = FALSE) ngrams_dictionary(texts, clean = TRUE, ngram_min = 2, ngram_max = 2)texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") ngrams_dictionary(texts) ngrams_dictionary(texts, clean = FALSE) ngrams_dictionary(texts, clean = TRUE, ngram_min = 2, ngram_max = 2)
ngrams_segmentation segments input sequence into possible segmented
text based on n-grams segmentation approach.
ngrams_segmentation( sequences, ngrams_dictionary, retrieve = "most-scored", simplify = TRUE, omit_zero = TRUE, score_formula = "points / words.number ^ 2" )ngrams_segmentation( sequences, ngrams_dictionary, retrieve = "most-scored", simplify = TRUE, omit_zero = TRUE, score_formula = "points / words.number ^ 2" )
sequences |
character vector, sequence to be segmented (e.g., hashtag) or without it. |
ngrams_dictionary |
data.frame, containing ids, n-grams to search, words to use for segmentation, and their points. See details. |
retrieve |
character vector of length 1, with formula to calculate score. |
simplify |
logical, if adjacent numbers should be merged into one, and underscores removed. See simplification section. |
omit_zero |
logical, if words with 0 points should be omitted from word count. See simplification section. |
score_formula |
character vector of length 1, with formula to calculate score. |
The output always will be data.frame. If retrieve='all'
is used, then the return will include all possible segmentation
of the given sequence.
If retrieve='first-shortest' is used, the first of the shortest
segmentations (with respect to the order of word's appearance
in the dictionary, 1 row).
If retrieve='most-pointed' is used, segmentation with most total
points is returned (1 row).
If retrieve='most-scored' is used, segmentation with the highest
score calculated as
(or as specified by the user).
The output is not in the input order. If needed, use
lapply
Dictionary has to be data.frame with four named columns: 1) to_search,
2) to_replace, 3) id, 4) points.
'to_search' should be column of type character, containing n-grams to
look for. Word case might be used.
'to_replace' should be column of type character, containing n-grams that
should be used for creating segmentation vector, if 'to_search' matches
text.
'id' should be column of type numeric, containing id of unigram.
'points' should be column of type numeric, containing number of points
for the word - the higher, the better. Unigrams with 0 points might be
removed from the word count with omit_zero argument. ngrams_dictionary
might be created with ngrams_dictionary.
Two arguments are possible for simplification:
simplify - removes spaces between numbers and removes underscores,
omit_zero - removes ids of 0-pointed unigrams,
and omits them in the word count.
By default segmented sequence will be simplified,
and numbers and underscores will be removed from word count
for score computing, since they are neutral as they are necessary.
texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") ndict <- ngrams_dictionary(texts) ngrams_segmentation("thisisscience", ndict) ngrams_segmentation("this_is_science", ndict) ngrams_segmentation("ThisIsScience", ndict) ngrams_segmentation("thisisscience", ndict, simplify=FALSE, omit_zero=FALSE)texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") ndict <- ngrams_dictionary(texts) ngrams_segmentation("thisisscience", ndict) ngrams_segmentation("this_is_science", ndict) ngrams_segmentation("ThisIsScience", ndict) ngrams_segmentation("thisisscience", ndict, simplify=FALSE, omit_zero=FALSE)
nuss returns the data.frame containing
hashtag, its segmented version, ids of dictionary words,
number of words it have taken to segment the hashtag,
total number of points, and computed score.
nuss(sequences, texts)nuss(sequences, texts)
sequences |
character vector, sequence to be segmented, (e.g., hashtag) or without it. Case-insensitive. |
texts |
character vector, these are the texts used to create n-grams and unigram dictionary. Case-insensitive. |
This function is an arbitrary combination of ngrams_dictionary, unigram_dictionary, ngrams_segmentation, unigram_sequence_segmentation, created to easily segment short texts based on text corpus.
The output always will be data.frame with sequences, that were
The output is not in the input order. If needed, use
lapply
texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") nuss(c("thisisscience", "scienceisscience"), texts)texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") nuss(c("thisisscience", "scienceisscience"), texts)
unigram_dictionary returns the data.frame containing dictionary for
unigram_sequence_segmentation.
unigram_dictionary(texts, points_filter = 1)unigram_dictionary(texts, points_filter = 1)
texts |
character vector, these are the texts used to create ngrams dictionary. Case-sensitive. |
points_filter |
numeric, sets the minimal number of points (occurrences) of an unigram to be included in the dictionary. |
The output always will be data.frame with 4 columns: 1) to_search, 2) to_replace, 3) id, 4) points.
texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") unigram_dictionary(texts)texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") unigram_dictionary(texts)
unigram_sequence_segmentation segments input sequence into possible segmented
text based on unigram sequence segmentation approach.
unigram_sequence_segmentation( sequences, unigram_dictionary = NUSS::base_dictionary, retrieve = "most-scored", simplify = TRUE, omit_zero = TRUE, score_formula = "points / words.number ^ 2" )unigram_sequence_segmentation( sequences, unigram_dictionary = NUSS::base_dictionary, retrieve = "most-scored", simplify = TRUE, omit_zero = TRUE, score_formula = "points / words.number ^ 2" )
sequences |
character vector, sequence to be segmented (e.g., hashtag). Case-sensitive. |
unigram_dictionary |
data.frame, containing ids, words to search, words to use for segmentation, and their points. See details. |
retrieve |
character vector of length 1, the type of the result data.frame to be returned: 'all', 'first-shortest', 'most-pointed' or 'most-scored'. See value section. |
simplify |
logical, if adjacent numbers should be merged into one, and underscores removed. See simplification section. |
omit_zero |
logical, if words with 0 points should be omitted from word count. See simplification section. |
score_formula |
character vector of length 1, with formula to calculate score. |
This function is not intended for long strings segmentation - 70 characters should be considered too long and may take hours to complete. 15 characters takes about 0.02s, 30 characters about 0.03s.
The output always will be data.frame. If retrieve='all'
is used, then the return will include all possible segmentation
of the given sequence.
If retrieve='first-shortest' is used, the first of the shortest
segmentations (with respect to the order of word's appearance
in the dictionary, 1 row).
If retrieve='most-pointed' is used, segmentation with most total
points is returned (1 row).
If retrieve='most-scored' is used, segmentation with the highest
score calculated as
(or as specified by the user).
The output is not in the input order. If needed, use
lapply
Dictionary has to be data.frame with four named columns: 1) to_search,
2) to_replace, 3) id, 4) points.
'to_search' should be column of type character, containing unigram to
look for. Word case might be used.
'to_replace' should be column of type character, containing word that
should be used for creating segmentation vector, if 'to_search' matches
text.
'id' should be column of type numeric, containing id of unigram.
'points' should be column of type numeric, containing number of points
for the word - the higher, the better. Unigrams with 0 points might be
removed from the word count with omit_zero argument.
Two arguments are possible for simplification:
simplify - removes spaces between numbers and removes underscores,
omit_zero - removes ids of 0-pointed unigrams,
and omits them in the word count.
By default segmented sequence will be simplified,
and numbers and underscores will be removed from word count
for score computing, since they are neutral as they are necessary.
# With custom dictionary texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") udict <- unigram_dictionary(texts) unigram_sequence_segmentation('thisisscience', udict) # With built-in dictionary (English, only lowercase) unigram_sequence_segmentation('thisisscience') unigram_sequence_segmentation('thisisscience2024') unigram_sequence_segmentation('thisisscience2024', simplify=FALSE, omit_zero=FALSE)# With custom dictionary texts <- c("this is science", "science is #fascinatingthing", "this is a scientific approach", "science is everywhere", "the beauty of science") udict <- unigram_dictionary(texts) unigram_sequence_segmentation('thisisscience', udict) # With built-in dictionary (English, only lowercase) unigram_sequence_segmentation('thisisscience') unigram_sequence_segmentation('thisisscience2024') unigram_sequence_segmentation('thisisscience2024', simplify=FALSE, omit_zero=FALSE)