PYTHON

Consider the following sentences written in Klingon. For each sentence, the part of speech of each word has been given (for ease of translation, some prefixes/suffixes have been treated as words), along with a translation. Using these training sentences, were going tobuild a Hidden Markov Model(HMM)to predict the part of speech of an unknown sentence using the Viterbi algorithm.

N PRO V N PRO

paDaq ghah taH terangan e

room (inside) he is human of

The human is in the room

V N V N

jachuqmeH rojHom neH terangan

in orderto parley truce want human

The enemy commander wants a truce in order to parley

N V N CONJ N V N

tera ngan qIp puq eg puq qIp terangan

human bit child and child bit child

The child bit the human, and the human bit the child

Step 1: Creating the Emission probability table(emission.javaor emission.py)Create a Emission probability table by computingthe frequencies of each part of speech in thetable below for all POS tags. Well use a smoothing factor of 0.1 (as discussed in class) to make sure that no event is impossible; add this number to all of your observations. Sample table valuesof two parts of speechhave been shown.Probability(word|tag) = Count(word,tag) / Count(tag)