>
> The documentation on feature extraction is up-to-date, quite complete
> and has an example snippet to use nltk for text pre-processing
> (lemmatization and tokenization):
>
>
> http://scikit-learn.org/dev/modules/feature_extraction.html#text-feature-extraction
Nice, but two issues:
1. WordNetLemmatizer is a bad example unless you can provide it with a POS
tag.
2. You might want to incorporate heterogenous features in text
classification, e.g. bag of named entities, syntactic dependencies,
document-level attributes, etc.
However this is just for text classification / clustering.
>
Which is all Tom wanted :)
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