Some of the NLP applications require splitting a large raw text into sentences to get more meaningful information out. Intuitively, a sentence is an acceptable unit of conversation. When it comes to computers, it is a harder task than it looks. A typical sentence splitter can be something as simple as splitting the string on (.), to something as complex as a predictive classifier to identify sentence boundaries:

>>>inputstring = ' This is an example sent. The sentence splitter will split on sent markers. Ohh really !!'
>>>from nltk.tokenize import sent_tokenize
>>>all_sent = sent_tokenize(inputstring)
>>>print all_sent
[' This is an example sent', 'The sentence splitter will split on markers.','Ohh really !!']

We are trying to split the raw text string into a list of sentences. The preceding function, sent_tokenize, internally uses a sentence boundary detection algorithm that comes pre-built into NLTK. If your application requires a custom sentence splitter, there are ways that we can train a sentence splitter of our own:

>>>import nltk.tokenize.punkt
>>>tokenizer = nltk.tokenize.punkt.PunktSentenceTokenizer()

The preceding sentence splitter is available in all the 17 languages. You just need to specify the respective pickle object. In my experience, this is good enough to deal with a variety of the text corpus, and there is a lesser chance that you will have to build your own.