python - Will this data mining approach work? Is it a good idea?
I am not sure if using a classifier is the best way to approach this problem.  If it is something which can be easily extracted using regex, then that is the best way to do it. If however, you want to use classifiers, here are two questions you need to ask yourself.

One, what does the unlabelled data look like and can you design good features from it? Depending on the kind of feature vector you design, the complexity of the classification task may range from very easy, to impossible. (A perceptron cannot solve XOR usually, except when you provide it with specific linear combinations of the input variable).

Two, what does the labelled data look like? Is it representative of the entire dataset or does it only contain very specific types of format? If it is the former, then your classifier will not work well on files which are not represented in the labelled data. 

If you just want to test run a classifier first, you can solve the problem of having more features than training samples by using Regularization. Regularization forces the training algorithm of the classifier to accept the simplest possible solution (think occam's razor). 

Almost all Machine Learning related packages in Python will have regularization options you can use, so enjoy.