A certain little-known human language consists of only nouns and verbs. A sentence begins with a noun with probability 75% and with a verb with probability 25%. If a given word in a sentence is a noun, there is a 75% probability that the next word is a verb and a 25% probability that the next word is a noun. If a given word is a verb, there is a 75% probability that the next word in the sentence is a noun, and a 25% probability that is a verb.

We can model this language using a Markov chain with two states: noun and verb.

We can model this language using a Markov chain with two states: noun and verb. \\' To get some practice, draw the Markov chain that models this language on a piece of paper. The Markov chain has... (mark all that apply} C] exactly 2 nodes and 4 directed edges C] exactly 2 nodes and 4 undirected edges C] exactly 2 nodes and 2 directed edges C] an edge from "noun" to "verb" that has a weight of 0.?5 C] an edge from "verb" to "noun" that has a weight of 0.25 What is the probability that the first three words of a sentence are all nouns? O around 1.56% around 4.69% O around 14.06% O around 42.19%3' What is the probability that the third word of a sentence is a noun? O 21.8755342- 25% O 56.25% 0 315% Finally, consider observing the sentence "Yacc grep." What is the most likely part-of-speech state sequence (i.e. sequence of nouns and verbs) that could have resulted in this sentence? As an exercise for now, just use a brute-force search over all 4 possible sequences, i.e. compute all 4 and choose the one with the highest probability. In the next lecture, we will see how to do this more efciently using the Viterbi algorithm. 0 Noun Noun Noun Verb O Verb Noun 0 Verb Verb