所有文档的成对地球移动距离（word2vec 表示）

Question

Is there a library that will take a list of documents and en masse compute the nxn matrix of distances - where the word2vec model is supplied? I can see that genism allows you to do this between two documents - but I need a fast comparison across all docs. like sklearns cosine_similarity.

Answer 1

The "Word Mover's Distance" (earth-mover's distance applied to groups of word-vectors) is a fairly involved optimization calculation dependent on every word in each document.

I'm not aware of any tricks that would help it go faster when calculating many at once – even many distances to the same document.

So the only thing needed to calculate pairwise distances are nested loops to consider each (order-ignoring unique) pairing.

For example, assuming your list of documents (each a list-of-words) is docs , a gensim word-vector model in model , and numpy imported as np , you could calculate the array of pairwise distances D with:

D = np.zeros((len(docs), len(docs)))
for i in range(len(docs)):
    for j in range(len(docs)):
        if i == j:
            continue  # self-distance is 0.0
        if i > j:
            D[i, j] = D[j, i]  # re-use earlier calc
        D[i, j] = model.wmdistance(docs[i], docs[j])

It may take a while, but you'll then have all pairwise distances in array D.

Answer 2

On top of the accepted answer you may want to use the faster wmd library wmd-relax .

The example then could be adjusted to:

D[i, j] = docs[i].similarity(docs[j])