Advanced Database Management System - Tutorials and Notes: POS tagging using HMM solved exercise

Wednesday, 1 April 2020

POS tagging using HMM solved exercise

Part-of-speech tagging using Hidden Markov Model solved exercise, find the probability value of the given word-tag sequence, how to find the probability of a word sequence for a POS tag sequence



POS Tagging using Hidden Markov Model - Solved Exercise

Question:
A Hidden Markov Model (HMM) is given in the table below;
Transition probabilities
Emission probabilities
P(NOUN|PRON)=0.001
P(PRON|START)=0.5
P(VERB|AUX)=0.5
P(AUX|PRON)=0.2
P(NOUN|AUX)=0.001
P(VERB|NOUN)=0.2
P(NOUN|NOUN)=0.1
P(she|PRON)=0.1
P(run|VERB)=0.01
P(can|AUX)=0.2
P(can|NOUN)=0.001
P(run|NOUN)=0.001

Calculate the probability P(she|PRON can|AUX run|VERB). [ Or, calculate the probability P(she can run, PRON AUX VERB)]

Solution:
The following graph is extracted from the given HMM, to calculate the required probability;
The probability of the given sentence can be calculated using the given bi-gram probabilities as follow;
P(she|PRON can|AUX run|VERB)
= P(PRON|START) * P(she|PRON) * P(AUX|PRON) * P(can|AUX) * P(VERB|AUX) * P(run|VERB)
= 0.5 * 0.1 * 0.2 * 0.2 * 0.5 * 0.01
= 0.00001
= 10-5
We arrived at this value by multiplying the transition and emission probabilities.

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