python - Compare similarity of terms/expressions using NLTK?

Question

I'm trying to compare terms/expressions which would (or not) be semantically related - these are not full sentences, and not necessarily single words; e.g. -

'Social networking service' and 'Social network' are clearly strongly related, but how to i quantify this using nltk?

Clearly i'm missing something as even the code:

w1 = wordnet.synsets('social network')

returns an empty list.

Any advice on how to tackle this?

Answer 1

There are some measures of semantic relatedness or similarity, but they're better defined for single words or single expressions in wordnet's lexicon - not for compounds of wordnet's lexical entries, as far as I know.

This is a nice web implementation of many similarity wordnet-based measures

• http://wn-similarity.sourceforge.net/

Some further reading on interpreting compounds using wordnet similarity (although not evaluating similarity on compounds), if you're interested:

• CiteSeerX (citations are clearer)
• Same article, PDF