can we learn a model to pre-process text?
Based on the details given in the comments, it looks like what you need is a binary classifier which predicts whether or not to remove a "numerical point". This means that each instance is an individual "numerical point", and you would need to annotate a sample of instances to use as a training set.

A simple option is to use a bag of words representation, i.e. represent every instance as a vector over the vocabulary where each cell contains the frequency of the corresponding word (or other variants, e.g. binary or TF-IDF weights). Then many classification algorithms can be used: decision trees, SVM, Naive Bayes, etc.