# pmi: Calculate Pointwise Mutual Information (PMI). In polmineR: Verbs and Nouns for Corpus Analysis

 pmi R Documentation

## Calculate Pointwise Mutual Information (PMI).

### Description

Calculate Pointwise Mutual Information as an information-theoretic approach to find collocations.

### Usage

pmi(.Object, ...)

## S4 method for signature 'context'
pmi(.Object)

## S4 method for signature 'Cooccurrences'
pmi(.Object)

## S4 method for signature 'ngrams'
pmi(.Object, observed, p_attribute = p_attributes(.Object)[1])


### Arguments

 .Object An object. ... Arguments methods may require. observed A count-object with the numbers of the observed occurrences of the tokens in the input ngrams object. p_attribute The positional attribute which shall be considered. Relevant only if ngrams have been calculated for more than one p-attribute.

### Details

Pointwise mutual information (PMI) is calculated as follows (see Manning/Schuetze 1999):

I(x,y) = log\frac{p(x,y)}{p(x)p(y)}

The formula is based on maximum likelihood estimates: When we know the number of observations for token x, o_{x}, the number of observations for token y, o_{y} and the size of the corpus N, the propabilities for the tokens x and y, and for the co-occcurence of x and y are as follows:

p(x) = \frac{o_{x}}{N}

p(y) = \frac{o_{y}}{N}

The term p(x,y) is the number of observed co-occurrences of x and y.

Note that the computation uses log base 2, not the natural logarithm you find in examples (e.g. https://en.wikipedia.org/wiki/Pointwise_mutual_information).

### References

Manning, Christopher D.; Schuetze, Hinrich (1999): Foundations of Statistical Natural Language Processing. MIT Press: Cambridge, Mass., pp. 178-183.

Other statistical methods: chisquare(), ll(), t_test()

### Examples

y <- cooccurrences("REUTERS", query = "oil", method = "pmi")
N <- size(y)[["partition"]]
I <- log2((y[["count_coi"]]/N) / ((count(y) / N) * (y[["count_partition"]] / N)))
use("polmineR")
use(pkg = "RcppCWB", corpus = "REUTERS")

dt <- decode(
"REUTERS",
p_attribute = "word",
s_attribute = character(),
to = "data.table",
verbose = FALSE
)
n <- ngrams(dt, n = 2L, p_attribute = "word")
obs <- count("REUTERS", p_attribute = "word")
phrases <- pmi(n, observed = obs)


polmineR documentation built on Nov. 2, 2023, 5:52 p.m.