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Can you provide psudocode or code for this? I'd post a snippet of my implementation but I'm too dumb to figure out the format. But wow this is much better for

• visarga 7y ago
  Something like this, including thresholding for pairs of words that have low dot product: import numpy as np def inner_product_rank(vecs, threshold=0.5): sims_mat = np.dot(vecs, vecs.transpose()) sims_mat = sims_mat - threshold sims_mat[sims_mat<0] = 0 ranks = np.sum(sims_mat, axis=0) return ranks Then you can take a weighted sum of the vecs or use the ranks to select the most related words. It is also possible to run spectral clustering on sims_mat to get the main topics of the text, it works quite well.