PYTHON Consider the following sentences written in Klingon. For each sentence, t
ID: 3858766 • Letter: P
Question
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, we’re 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
pa’Daq ghah taH tera’ngan ’e
room (inside) he is human of
The human is in the room
V N V N
ja’chuqmeH rojHom neH tera’ngan
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 tera’ngan
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. We’ll 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)
Explanation / Answer
egin{tabular}{llllll} N & PRO & V & N & PRO \ pa'Daq & ghah & taH & tera'ngan & 'e \ room (inside) & he & is & human & of \ multicolumn{6}{c}{{em The human is in the room}} \ end{tabular} egin{tabular}{llll} V & N & V & N \ ja'chuqmeH & rojHom & neH & tera'ngan \ in order to parley & truce & want & human \ multicolumn{4}{c}{{em The enemy commander wants a truce in order to parley}} end{tabular} egin{tabular}{lllllll} N & V & N & CONJ & N & V & N \ tera'ngan & qIp & puq & 'eg & puq & qIp & tera'ngan \ human & bit & child & and & child & bit & child \ multicolumn{7}{c}{{em The child bit the human, and the human bit the child}} end{tabular}
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