extraction information from resume I have a project in machine learning in which I need to analyze a
  curriculum vitae. for that I have to write a python program. It uses
  basic techniques of Natural Language Processing like word
  parsing,chunking,reg ex parser. If you run the algorithm you can
  easily capture information like name,email id,address,educational
  qualification,experience in seconds from a large number of documents.
  but I have a confusion between two methods LSTM (RNN) and NER? What do
  you think is the best method?
LSTM is a neural network architecture used for sequence prediction, whereas NER is name of NLP task.

The two are incomparable, as one is a method and the other type of problem.

The relationship between them is that LSTMs can can be used for sequence tagging, which can be used for NER.