You will learn to implement a POS tagger using CRF$.$ Complete each of the following tasks.

Load NLTK Treebank tagged sentences using nltk$.$corpus.treebank.tagged$\_$sents$().$

Use first $80\%$ of the sentences for training and the remaining $20\%$ for the testing.

Extract the word and the tag from each of the sentences and create a vocabulary of all

the words and a set of all tags.

Build the following feature set for each token$/$word:

The current token$/$word

Is the word a number? $($boolean value$)$

Does the word contain any hyphens? $($boolean value$)$

Is the word all uppercase? $($boolean value$)$

Does the word have any uppercase letters? $($boolean value$)$

Is the word all lowercase? $($boolean value$)$

Length of the word

Bigrams of the word

Use the CRF model from sklearn$\_$crfsuite library ${?}^6,$ and train it with feature set built

above.

Evaluate the performance of the model in terms of accuracy on the test set.