textstat_collocations: Identify and score multi-word expressions
In koheiw/quanteda.core: Quantitative Analysis of Textual Data

Description Usage Arguments Details Value Note Author(s) References Examples

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

textstat_collocations(
  x,
  method = "lambda",
  size = 2,
  min_count = 2,
  smoothing = 0.5,
  tolower = TRUE,
  ...
)

is.collocations(x)

`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. Currently this is limited to `"lambda"`. 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
`...`	additional arguments passed to `tokens()`, if `x` is not a tokens object already

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}, …, j_{K})=c such that the kth element of c is 1 if the kth word in z is equal to the kth word in x, and 0 otherwise. Let c_{i}=(j_{i1}, …, j_{iK}), i=1, …, 2^{K}=M, be the possible values of c(x,z), with c_{M}=(1,1, …, 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,…,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}.

λ: The K-way interaction parameter in the saturated loglinear model fitted to the n_{i}. It can be calculated as

λ = ∑_{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{λ}{[∑_{i=1}^{M} n_{i}^{-1}]^{(1/2)}}

textstat_collocations returns a data.frame of collocations and their scores and statistics. This consists of the collocations, their counts, length, and λ and z statistics. When size is a vector, then count_nested counts the lower-order collocations that occur within a higher-order collocation (but this does not affect the statistics).

is.collocation returns TRUE if the object is of class collocations, FALSE otherwise.

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

Kenneth Benoit, Jouni Kuha, Haiyan Wang, and Kohei Watanabe

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.

corp <- data_corpus_inaugural[1:2]
head(cols <- textstat_collocations(corp, size = 2, min_count = 2), 10)
head(cols <- textstat_collocations(corp, size = 3, min_count = 2), 10)

# extracting multi-part proper nouns (capitalized terms)
toks1 <- tokens(data_corpus_inaugural)
toks2 <- tokens_remove(toks1, pattern = stopwords("english"), padding = TRUE)
toks3 <- tokens_select(toks2, pattern = "^([A-Z][a-z\\-]{2,})", valuetype = "regex",
                       case_insensitive = FALSE, padding = TRUE)
tstat <- textstat_collocations(toks3, size = 3, tolower = FALSE)
head(tstat, 10)

# vectorized size
txt <- c(". . . . a b c . . a b c . . . c d e",
         "a b . . a b . . a b . . a b . a b",
         "b c d . . b c . b c . . . b c")
textstat_collocations(txt, size = 2:3)