## Made in Python
Used the following modules:
* nltk
* flask

NLTK
nltk.tag.tnt
TnT - Statistical POS tagger

TnT uses a second order Markov model to produce tags for a sequence of input
The set of possible tags for a given word is derived from the training data.
(Training Data: It is the set of all tags that exact word has been assigned.)
TnT DOES NOT AUTOMATICALLY DEAL WITH UNSEEN WORDS
TnT SHOULD BE USED WITH SENTENCE-DELIMITED INPUT
Input for tag function is a single sentence Input for tagdata function is a list of sentences . Output is of a similar form ('WORD' , 'TAG')

WORKING
The set of possible tags for a given word is derived from the training data. It is the set of all tags that exact word has been assigned.
The probability of a tag for a given word is the linear interpolation of 3 markov models; a zero-order, first-order, and a second order model.

Functions used
* word_tokenize(s)
Tokenize a string to split off punctuation other than periods
* train(data)
Uses a set of tagged data to train the tagger.
* tag(data)
Determine the most appropriate tag sequence for the given token sequence, and return a corresponding list of tagged tokens. A tagged token is encoded as a tuple ('WORD' , 'TAG')
* evaluate(gold)
Score the accuracy of the tagger against the gold standard. Strip the tags from the gold standard text, retag it using the tagger, then compute the accuracy score.