How To Normalize Similarity Measures From Wordnet

I am trying to calculate semantic similarity between two words. I am using Wordnet-based similarity measures i.e Resnik measure(RES), Lin measure(LIN), Jiang and Conrath measure(JN

Solution 1:

How to normalize a single measure

Let's consider a single arbitrary similarity measure M and take an arbitrary word w.

Define m = M(w,w). Then m takes maximum possible value of M.

Let's define MN as a normalized measure M.

For any two words w, u you can compute MN(w, u) = M(w, u) / m.

It's easy to see that if M takes non-negative values, then MN takes values in [0, 1].

How to normalize a measure combined from many measures

In order to compute your own defined measure F combined of k different measures m_1, m_2, ..., m_k first normalize independently each m_i using above method and then define:

alpha_1, alpha_2, ..., alpha_k

such that alpha_i denotes the weight of i-th measure.

All alphas must sum up to 1, i.e:

alpha_1 + alpha_2 + ... + alpha_k = 1

Then to compute your own measure for w, u you do:

F(w, u)= alpha_1 * m_1(w, u)+ alpha_2 * m_2(w, u)+ ... + alpha_k * m_k(w, u)

It's clear that F takes values in [0,1]