Identify and score multi-word expressions, or adjacent fixed-length collocations, from text.

textstat_collocationsdev(x, method = "all", size = 2, min_count = 2,
  smoothing = 0.5, tolower = TRUE, show_counts = FALSE, ...)

is.collocationsdev(x)

Arguments

x

a character, corpus, or tokens object whose collocations will be scored. The tokens object should include punctuation, and if any words have been removed, these should have been removed with padding = TRUE. While identifying collocations for tokens objects is supported, you will get better results with character or corpus objects due to relatively imperfect detection of sentence boundaries from texts already tokenized.

method

association measure for detecting collocations: "all", "lambda", "lambda1", "lr", "chi2", and "dice". See Details.

size

integer; the length of the collocations to be scored

min_count

numeric; minimum frequency of collocations that will be scored

smoothing

numeric; a smoothing parameter added to the observed counts (default is 0.5)

tolower

logical; if TRUE, form collocations as lower-cased combinations

show_counts

logical; if TRUE, output observed and expected counts

...

additional arguments passed to tokens, if x is not a tokens object already

Value

textstat_collocationsdev returns a data.frame of collocations and their scores and statistsics. is.collocationdev returns TRUE if the object is of class collocationsdev, FALSE otherwise.

Details

Documents are grouped for the purposes of scoring, but collocations will not span sentences. If x is a tokens object and some tokens have been removed, this should be done using tokens_remove(x, pattern, padding = TRUE) so that counts will still be accurate, but the pads will prevent those collocations from being scored.

The lambda computed for a size = \(K\)-word target multi-word expression the coefficient for the \(K\)-way interaction parameter in the saturated log-linear model fitted to the counts of the terms forming the set of eligible multi-word expressions. This is the same as the "lambda" computed in Blaheta and Johnson's (2001), where all multi-word expressions are considered (rather than just verbs, as in that paper). The z is the Wald \(z\)-statistic computed as the quotient of lambda and the Wald statistic for lambda as described below.

In detail:

Consider a \(K\)-word target expression \(x\), and let \(z\) be any \(K\)-word expression. Define a comparison function \(c(x,z)=(j_{1}, \dots, j_{K})=c\) such that the \(k\)th element of \(c\) is 1 if the \(k\)th word in \(z\) is equal to the \(k\)th word in \(x\), and 0 otherwise. Let \(c_{i}=(j_{i1}, \dots, j_{iK})\), \(i=1, \dots, 2^{K}=M\), be the possible values of \(c(x,z)\), with \(c_{M}=(1,1, \dots, 1)\). Consider the set of \(c(x,z_{r})\) across all expressions \(z_{r}\) in a corpus of text, and let \(n_{i}\), for \(i=1,\dots,M\), denote the number of the \(c(x,z_{r})\) which equal \(c_{i}\), plus the smoothing constant smoothing. The \(n_{i}\) are the counts in a \(2^{K}\) contingency table whose dimensions are defined by the \(c_{i}\). \(\lambda\): The \(K\)-way interaction parameter in the saturated loglinear model fitted to the \(n_{i}\). It can be calculated as $$\lambda = \sum_{i=1}^{M} (-1)^{K-b_{i}} * log n_{i}$$

where \(b_{i}\) is the number of the elements of \(c_{i}\) which are equal to 1.

Wald test \(z\)-statistic \(z\) is calculated as: $$z = \frac{\lambda}{[\sum_{i=1}^{M} n_{i}^{-1}]^{(1/2)}}$$

Note

This function is under active development, with more measures to be added in the the next release of quanteda.

References

Blaheta, D., & Johnson, M. (2001). Unsupervised learning of multi-word verbs. Presented at the ACLEACL Workshop on the Computational Extraction, Analysis and Exploitation of Collocations.

Examples

txts <- data_corpus_inaugural[1:2] head(cols <- textstat_collocationsdev(txts, size = 2, min_count = 2), 10)
#> collocation count length lambda z G2 chi2 pmi #> 1 , and 17 2 2.643957 8.170237 49.47743 108.46212 2.927463 #> 2 have been 5 2 5.731000 7.487958 43.20136 399.03760 6.200685 #> 3 of the 24 2 1.781820 6.830093 37.22476 58.28699 1.935835 #> 4 has been 3 2 5.717327 6.584944 28.52046 323.74321 6.548608 #> 5 i have 5 2 3.772416 6.461199 26.86011 113.55789 4.463719 #> 6 , i 10 2 2.570085 6.377237 29.25016 65.92607 2.956032 #> 7 will be 4 2 3.974267 6.109305 23.64307 112.94349 4.728587 #> 8 less than 2 2 6.431212 5.663496 23.15338 373.56773 7.233106 #> 9 public good 2 2 6.431212 5.663496 23.15338 373.56773 7.233106 #> 10 which i 6 2 2.657154 5.555529 19.98871 52.21109 3.264154 #> LFMD #> 1 11.186029 #> 2 11.119548 #> 3 11.165255 #> 4 10.163318 #> 5 9.382582 #> 6 9.740667 #> 7 9.068437 #> 8 9.876962 #> 9 9.876962 #> 10 8.665034
head(cols <- textstat_collocationsdev(txts, size = 3, min_count = 2), 10)
#> collocation count length lambda z G2 chi2 pmi #> 1 of which the 2 3 6.179554 2.8579715 13.4611112 23.7539935 3.2516278 #> 2 , and of 2 3 3.066282 1.7161287 4.0540624 3.9852233 1.2377281 #> 3 in which i 3 3 2.907704 1.5893955 3.4809716 3.0412877 0.7012360 #> 4 , or by 2 3 3.086502 1.3263061 2.2762489 1.9886129 0.5716844 #> 5 i have in 2 3 2.484260 1.1250830 1.6346876 1.4070556 0.4984132 #> 6 me by the 2 3 2.362269 1.0839184 1.5158738 1.3075711 0.4867261 #> 7 , and the 3 3 1.017118 1.0243655 1.0678760 1.0779195 0.5158313 #> 8 and of the 2 3 1.057485 0.8988065 0.8416763 0.8445156 0.5606277 #> 9 , i shall 3 3 1.661358 0.7605286 0.6951628 0.6084811 0.1996503 #> 10 . on the 2 3 1.014510 0.5884358 0.3960617 0.3629160 0.2626685 #> LFMD #> 1 5.895484 #> 2 3.881584 #> 3 4.315946 #> 4 3.215541 #> 5 3.142269 #> 6 3.130582 #> 7 4.130541 #> 8 3.204484 #> 9 3.814360 #> 10 2.906525
# extracting multi-part proper nouns (capitalized terms) toks2 <- tokens(data_corpus_inaugural) toks2 <- tokens_remove(toks2, stopwords("english"), padding = TRUE) toks2 <- tokens_select(toks2, "^([A-Z][a-z\\-]{2,})", valuetype = "regex", case_insensitive = FALSE, padding = TRUE) seqs <- textstat_collocationsdev(toks2, size = 3, tolower = FALSE) head(seqs, 10)
#> collocation count length lambda z G2 #> 1 United States Congress 2 3 -2.152404 -1.014623 0.7972545 #> 2 Vice President Bush 2 3 -11.582818 -4.471125 9.6364697 #> chi2 pmi LFMD #> 1 1.182867 -0.1873977 2.456458 #> 2 9474.743454 -0.2634959 2.380360