keywords_collocation: Extract collocations - a sequence of terms which follow each...

In udpipe: Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the 'UDPipe' 'NLP' Toolkit

Extract collocations - a sequence of terms which follow each other

Description

Collocations are a sequence of words or terms that co-occur more often than would be expected by chance. Common collocation are adjectives + nouns, nouns followed by nouns, verbs and nouns, adverbs and adjectives, verbs and prepositional phrases or verbs and adverbs.
This function extracts relevant collocations and computes the following statistics on them which are indicators of how likely two terms are collocated compared to being independent.

• PMI (pointwise mutual information): log2(P(w1w2) / P(w1) P(w2))

• MD (mutual dependency): log2(P(w1w2)^2 / P(w1) P(w2))

• LFMD (log-frequency biased mutual dependency): MD + log2(P(w1w2))

As natural language is non random - otherwise you wouldn't understand what I'm saying, most of the combinations of terms are significant. That's why these indicators of collocation are merely used to order the collocations.

Usage

keywords_collocation(x, term, group, ngram_max = 2, n_min = 2, sep = " ")

collocation(x, term, group, ngram_max = 2, n_min = 2, sep = " ")

Arguments

x	a data.frame with one row per term where the sequence of the terms correspond to the natural order of a text. The data frame x should also contain the columns provided in term and group
term	a character vector with 1 column from x which indicates the term
group	a character vector with 1 or several columns from x which indicates for example a document id or a sentence id. Collocations will be computed within this group in order not to find collocations across sentences or documents for example.
ngram_max	integer indicating the size of the collocations. Defaults to 2, indicating to compute bigrams. If set to 3, will find collocations of bigrams and trigrams.
n_min	integer indicating the frequency of how many times a collocation should at least occur in the data in order to be returned. Defaults to 2.
sep	character string with the separator which will be used to paste together terms which are collocated. Defaults to a space: ' '.

Value

a data.frame with columns

• keyword: the terms which are combined as a collocation

• ngram: the number of terms which are combined

• left: the left term of the collocation

• right: the right term of the collocation

• freq: the number of times the collocation occurred in the data

• freq_left: the number of times the left element of the collocation occurred in the data

• freq_right: the number of times the right element of the collocation occurred in the data

• pmi: the pointwise mutual information

• md: mutual dependency

• lfmd: log-frequency biased mutual dependency

Examples

data(brussels_reviews_anno)
x      <- subset(brussels_reviews_anno, language %in% "fr")
colloc <- keywords_collocation(x, term = "lemma", group = c("doc_id", "sentence_id"), 
                               ngram_max = 3, n_min = 10)
head(colloc, 10)

## Example on finding collocations of nouns preceded by an adjective
library(data.table)
x <- as.data.table(x)
x <- x[, xpos_previous := txt_previous(xpos, n = 1), by = list(doc_id, sentence_id)]
x <- x[, xpos_next     := txt_next(xpos, n = 1),     by = list(doc_id, sentence_id)]
x <- subset(x, (xpos %in% c("NN") & xpos_previous %in% c("JJ")) | 
               (xpos %in% c("JJ") & xpos_next %in% c("NN")))
colloc <- keywords_collocation(x, term = "lemma", group = c("doc_id", "sentence_id"), 
                               ngram_max = 2, n_min = 2)
head(colloc)

udpipe documentation built on Jan. 6, 2023, 5:06 p.m.