Text classification using deep learning with removing name, place address etc I want to prepare a deep learning model for text classification(Document classification), But in my training content many place have name, address, brand name, etc.. which will do confuse to model as deep learning is work with sequence of words, I was thinking if we will train model by removing these details, but in predict content when we get name, address, brand name then it also confuse...
Can someone give the idea how i handle this situation in deep learning?
And which deep learning algorithm and library should use for best accuracy and speed for text classification.
Normally if you have enough training data this shouldn't be an issue, because the model will realize that these named entities are not relevant for classifying the text.
Anyway if you want to remove them you would need to first detect them with Named Entity Recognition, there are many implementations. Then you can simply remove them.
Using DL classification as a first option is questionable imho:

it usually requires larger training data
it takes much longer to train than traditional models
it's harder to understand what happens when things go wrong

I'm old school so I'd recommend starting with simple methods, for instance TFIDF vectors with Naive Bayes or Decision Trees.