Pointwise mutual information
This article provides insufficient context for those unfamiliar with the subject.Learn how and when to remove this template message)(February 2012) (
Pointwise mutual information (PMI), or point mutual information, is a measure of association used in information theory and statistics. In contrast to mutual information (MI) which builds upon PMI, it refers to single events, whereas MI refers to the average of all possible events.
The PMI of a pair of outcomes x and y belonging to discrete random variables X and Y quantifies the discrepancy between the probability of their coincidence given their joint distribution and their individual distributions, assuming independence. Mathematically:
The mutual information (MI) of the random variables X and Y is the expected value of the PMI over all possible outcomes (with respect to the joint distribution ).
The measure is symmetric (). It can take positive or negative values, but is zero if X and Y are independent. Note that even though PMI may be negative or positive, its expected outcome over all joint events (MI) is positive. PMI maximizes when X and Y are perfectly associated (i.e. or ), yielding the following bounds:
Finally, will increase if is fixed but decreases.
Here is an example to illustrate:
Using this table we can marginalize to get the following additional table for the individual distributions:
With this example, we can compute four values for . Using base-2 logarithms:
(For reference, the mutual information would then be 0.2141709)
Similarities to mutual information
Pointwise Mutual Information has many of the same relationships as the mutual information. In particular,
Where is the self-information, or .
Normalized pointwise mutual information (npmi)
In addition to the above-mentioned npmi, PMI has many other interesting variants. A comparative study of these variants can be found in 
Chain-rule for pmi
This is easily proven by:
In computational linguistics, PMI has been used for finding collocations and associations between words. For instance, countings of occurrences and co-occurrences of words in a text corpus can be used to approximate the probabilities and respectively. The following table shows counts of pairs of words getting the most and the least PMI scores in the first 50 millions of words in Wikipedia (dump of October 2015) filtering by 1,000 or more co-occurrences. The frequency of each count can be obtained by dividing its value by 50,000,952. (Note: natural log is used to calculate the PMI values in this example, instead of log base 2)
|word 1||word 2||count word 1||count word 2||count of co-occurrences||PMI|
Good collocation pairs have high PMI because the probability of co-occurrence is only slightly lower than the probabilities of occurrence of each word. Conversely, a pair of words whose probabilities of occurrence are considerably higher than their probability of co-occurrence gets a small PMI score.
- Kenneth Ward Church and Patrick Hanks (March 1990). "Word association norms, mutual information, and lexicography". Comput. Linguist. 16 (1): 22–29.
- Bouma, Gerlof (2009). "Normalized (Pointwise) Mutual Information in Collocation Extraction" (PDF). Proceedings of the Biennial GSCL Conference.
- Francois Role, Moahmed Nadif. Handling the Impact of Low frequency Events on Co-occurrence-based Measures of Word Similarity:A Case Study of Pointwise Mutual Information. Proceedings of KDIR 2011 : KDIR- International Conference on Knowledge Discovery and Information Retrieval, Paris, October 26-29 2011
- Paul L. Williams. INFORMATION DYNAMICS: ITS THEORY AND APPLICATION TO EMBODIED COGNITIVE SYSTEMS (PDF).
- Fano, R M (1961). "chapter 2". Transmission of Information: A Statistical Theory of Communications. MIT Press, Cambridge, MA. ISBN 978-0262561693.
- Demo at Rensselaer MSR Server (PMI values normalized to be between 0 and 1)