So far, just a simple script to run a bunch of sklearn clustering algorithms on embeddings in word2vec format.
Some results on usefulness:
- usefulness for POS tagging: https://github.com/GateNLP/exp-lf-pos
Get usage information by running:
python3 ./python/cluster-embs.py -h
Currently the following clustering algorithms are supported (using the sklearn back-end). For
some of the algorithms, information about the clusters is written to a file with extension info.json:
- MiniBatchKMeans (default): k-means clustering using minibatch SGD
Info:
cluster_centers,inertia,counts,n_iter - KMeans: k-means clustering
Info:
cluter_centers,inertia,n_iter - AgglomerativeClusteringWard: agglomerative clustering using ward linkage
- AgglomerativeClusteringAverageEuclidean: agglomerative clustering using average linkage
- Birch
- SpectralClustering: spectral clustering with rbf affinity
For MiniBatchKMeans and KMeans, the 20 elements most similar to each of the k cluster centroids
are stored in a file with the extension mostsimilar.json.
| embeddings | machine | alg | k | elapsed time, total | elapsed time, clustering |
|---|---|---|---|---|---|
| fasttext wiki.en.vec | derwent | KMeans | 100 | 5:21:16 | ??? |
| fasttext wiki.en.vec | derwent | MiniBatchKMeans | 100 | 0:16:37 | 0:00:38 |
| fasttext wiki.en.vec | derwent | MiniBatchKMeans | 500 | 0:22:18 | 0:02:22 |
| fasttext wiki.bg.vec | derwent | MiniBatchKMeans | 100 | 0:02:27 | 0:00:13 |
| fasttext wiki.bg.vec | derwent | MiniBatchKMeans | 500 | 0:04:42 | 0:01:18 |