information content — Wordnet

I was trying to rewrite(badly needed) my M.Sc thesis, and had to go traipsing through the paper trails involved for information content calculations.
It turns out it is calculated by -Log(P(Concept)) (as per this paper)
Here P(x) ==> Probability of X.

And in the case of the nltk.wordnet package the probability is calculated directly in reference from a given corpus with the straightforward n(Concept) / N(Concepts in corpus).
n(Concept) — number of occurences of the Concept in the corpus
N(Concepts in corpus) — Size of the corpus in terms of Concepts/sensets.

Now, my innately(insanely?) curious brain goes off why those specific choices? i.e: negative log and probability calculation.

1. The -ve is mostly convenience. Since all probabilities are less than 1, if we use log to the base of 2, (which i presume is true in this case) the log results will always be negative, so a -ve makes sense.

2. Log, now here’s the interesting part, A log is essentially the inverse of an exponential function. And exponential functions blow-up/magnify relative differences(aka first-order difference). Which means a log will reduce them. So in effect if two points on the probability distribution are closer, (compared other two) they will move even closer.

3. P(x) — This one’s rather straightforward, as it is a simple ratio, it gives a good idea of which concepts are often and most used in a given corpus.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.