Read this paper for more details:
http://psiexp.ss.uci.edu/research/papers/SteyversShiffrinNelsonFormatted.pdf

WAS_BIGS2SOLUTION.mat
=====================

Contains SVD solution to the full 5018 x 5018 word association dataset. Take cosine similarity between vectors.
The SVD is based on S(2)_ij = S(1)_ij + sum_k[ S(1)_ik * S(1)_kj ] where S(1)_ij = A_ij + A_ji and A_ij is the
associative strength between item i and j.   

  N            1x1                      8  double   --> number of words           
  V         5018x400             16057600  double   --> V( i , : ) contains the vector for word i (not normalized).            
  W            1x5018              623076  cell     --> W{ i } contains word i
  

WAS_S2.mat
==========

Contains SVD solution to a subset of 2500 x 2500 word association dataset. Take cosine similarity between vectors.   
The SVD is based on S(2)_ij = S(1)_ij + sum_k[ S(1)_ik * S(1)_kj ] where S(1)_ij = A_ij + A_ji and A_ij is the
associative strength between item i and j.
       
  N            1x1                      8  double    --> number of words          
  V         2500x500             10000000  double    --> V( i , : ) contains the vector for word i (not normalized).           
  W            1x2500              307642  cell      --> W{ i } contains word i


WAS_MDS.mat
===========

Contains MDS solution to a subset of 2500 x 2500 word association dataset. Take Euclidean distance between vectors.   
The MDS is based on distances T_ij = -log[ S(1)_ik * S(1)_kl * ... * S(1)_qj ] where S(1)_ij = A_ij + A_ji and A_ij is the
associative strength between item i and j.

  N            1x1                      8  double   --> number of words           
  V         2500x500             10000000  double   --> V( i , : ) contains the vector for word i (not normalized).           
  W            1x2500              307642  cell     --> W{ i } contains word i