What NLP tools to use to match phrases having similar meaning or semantics

Question

I am working on a project which requires me to match a phrase or keyword with a set of similar keywords. I need to perform semantic analysis for the same.

an example:

Relevant QT
cheap health insurance
affordable health insurance
low cost medical insurance
health plan for less
inexpensive health coverage

Common Meaning

low cost health insurance

Here the the word under Common Meaning column should match the under Relevant QT column. I looked at a bunch of tools and techniques to do the same. S-Match seemed very promising, but I have to work in Python, not in Java. Also Latent Semantic Analysis looks good but I think its more for document classification based upon a Keyword rather than keyword matching. I am somewhat familiar with NLTK. Could someone provide some insight on what direction I should proceed and what tools I should use for the same?

Answer 1

If you have a big corpus, where these words occur, available, you can train a model to represent each word as vector. For instance, you can use deep learning via word2vec’s "skip-gram and CBOW models", they are implemented in the gensim software package

In the word2vec model, each word is represented by a vector, you can then measure the semantic similarity between two words by measuring the cosine of the vectors representing th words. Semantic similar words should have a high cosine similarity, for instance:

model.similarity('cheap','inexpensive') = 0.8

(The value is made up, just for illustration.)

Also, from my experiments, summing a relatively small number of words (i.e., up to 3 or 4 words) preserves the semantics, for instance:

vector1 = model['cheap']+model['health']+model['insurance']
vector2 = model['low']+model['cost']+model['medical']+model['insurance']

similarity(vector1,vector2) = 0.7

(Again, just for illustration.)

You can use this semantic similarity measure between words as a measure to generate your clusters.