udpipe (version 0.2)

cooccurrence: Create a cooccurence data.frame

Description

A cooccurence data.frame indicates how many times each term co-occurs with another term. This type of dataset is a data.frame with fields term1, term2 and cooc where cooc indicates how many times term1 and term2 co-occurred. The dataset can be constructed based upon a data frame where you look within a group if 2 terms occurred. It also can be constructed based upon a vector of words in which case we look how many times each word is followed by another word.

Usage

cooccurrence(x, order = TRUE, ...)
# S3 method for character
cooccurrence(x, order = TRUE, ...)
# S3 method for cooccurrence
cooccurrence(x, order = TRUE, ...)
# S3 method for data.frame
cooccurrence(x, order = TRUE, ..., group, term)

Arguments

x

either

• a data.frame where the data.frame contains 1 row per document/term, in which case you need to provide group and term. This uses cooccurrence.data.frame.

• a character vector with terms. This uses cooccurrence.character.

• an object of class cooccurrence.This uses cooccurrence.cooccurrence.

order

logical indicating if we need to sort the output from high cooccurrences to low coccurrences. Defaults to TRUE.

...

other arguments passed on to the methods

group

character string with a column in the data frame x. To be used if x is a data.frame.

term

character string with a column in the data frame x, containing 1 term per row. To be used if x is a data.frame.

Value

a data.frame with columns term1, term2 and cooc indicating for the combination of term1 and term2 how many times this combination occurred

Methods (by class)

• character: Create a cooccurence data.frame based on a vector of terms

• cooccurrence: Aggregate co-occurrence statistics by summing the cooc by term/term2

• data.frame: Create a cooccurence data.frame based on a data.frame where you look within a document / sentence / paragraph / group if terms co-occur

Examples

# NOT RUN {
data(brussels_reviews_anno)

## By document, which lemma's co-occur
x <- subset(brussels_reviews_anno, xpos %in% c("NN", "JJ") & language %in% "fr")
x <- cooccurrence(x, group = "doc_id", term = "lemma")
head(x)

## Which words follow each other
x <- c("A", "B", "A", "B", "c")
cooccurrence(x)

data(brussels_reviews_anno)
x <- subset(brussels_reviews_anno, language == "es")
x <- cooccurrence(x$lemma)
head(x)

## Which nouns follow each other in the same document
library(data.table)
x <- as.data.table(brussels_reviews_anno)
x <- subset(x, language == "nl" & xpos %in% c("NN"))
x <- x[, cooccurrence(lemma, order = FALSE), by = list(doc_id)]
head(x)

x_nodoc <- cooccurrence(x)
x_nodoc <- subset(x_nodoc, term1 != "appartement" & term2 != "appartement")
head(x_nodoc)
# }