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For supervised classification this tool is suitable when your dataset is large enough. I performed some tests with binary classification (twitter sentiment) on
by haddr 10y ago
For supervised classification this tool is suitable when your dataset is large enough. I performed some tests with binary classification (twitter sentiment) on the corpus with ~7.000 samples and the result is not impressive (~0.77). Vowpal wabbit performes slightly better here, with almost the same training time.
I'm looking forward to try it on some bigger datasets.
I also wonder if is it possible to use separately trained word vector model for the supervised task?
- nl 10y agoWhat is SOA on Twitter sentiment these days? I thought it was around 80%
- deleted 10y ago[deleted]
- exgrv 10y agoThanks for pointing this out. We design this library on large datasets and some static variables may not be well tuned for smaller ones. For example the learning rate is only updated every 10k words. We are fixing that now, could you please send us on which dataset you were testing? We would like to see if we have solved this.