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It seems like your cluster quality will be sensitive to the words used to seed each cluster. Why not use a standard word clustering algorithm like Brown cluste
by bravura 14y ago
It seems like your cluster quality will be sensitive to the words used to seed each cluster.
Why not use a standard word clustering algorithm like Brown clustering? http://acl.ldc.upenn.edu/J/J92/J92-4003.pdf http://acl.ldc.upenn.edu/J/J92/J92-4003.pdf
Percy Liang wrote a great implementation in C++ that you could plug into your visualization: http://cs.stanford.edu/~pliang/software/ http://cs.stanford.edu/~pliang/software/
Also of interest is that Brown clustering is hierarchical, so you can get coarse or fine-grained clustering.
[Aside: Here are some 2-d visualizations I made of word embeddings from a neural language model: http://metaoptimize.com/projects/wordreprs/ http://metaoptimize.com/projects/wordreprs/ ]
- omershapira 14y agoI'll definitely look into the Brown clustering. I used this one with the hope of being able to eventually run spectral clustering on this (main problem: the KLIC defines a pre-metric, not a proper metric, so the order of operations is crucial). How does the Brown clustering method guarantee less sensitivity to the seeding words? Your visualization is interesting, I'd like to able to navigate it in order overcome most of the clutter. Originally I thought of mapping the clusters in 3d "clouds", but I think the dataset is too large for making a dimension-based visualization more than recreational - I mean, I'd probably be happier to read a cluster as a list.