I have read this link.
I cannot comment on the original post. Therefore I ask here:
I have two variables are not normally distributed. I implement different methods to calculate the first variables in different formats, and calculate the Pearson's correlation between the first and the second variables.
If the Pearson's correlation calculated by Method A, is much higher than Method B. Can I say Method A is better than Method B?
If the Pearson correlation is very high between two not normally distributed variables, 0.8, for example. Can I say these two variables are linearly related?
This is my project for measuring the sense relatedness between synonyms
I extract sentences containing those synonyms, transfer the neighbouring words appearing in those sentences to vectors by different methods (e.g. TF-IDF, using PMI to do feature selection), calculate the cosine distance between different vectors. These are the first variables.
The second variables are the sense relatedness from this paper (Page 628)
Rubenstein, Herbert, and John B. Goodenough. "Contextual correlates of synonymy." Communications of the ACM 8.10 (1965): 627-633.