Assign Penn Treebank POS tags to the words in the following sentence: - Unit 5: Sequence tagging with MEMMs - Use this assignment in your class!

Unit 5: Sequence tagging with MEMMs

1.Assign Penn Treebank POS tags to the words in the following sentence:

2.What is the difference between an "adverb phrase" and an "adverbial"?

3.What are open class and closed class grammatical categories?

- Name at least two parts of speech that are open class.
- Name at least two parts of speech that are closed class.

4.What are the assumptions of a first-order hidden Markov model?

5.Using the given probability table, calculate the probability of the weather forecast for the next 7 days being "sunny-sunny-rainy-rainy-sunny-cloudy-sunny" given that today is sunny (i.e., the probability of it being sunny today is 1).

6.What are two components (probabilities) of an HMM tagger?

7.What are the advantages of using a MEMM instead of an HMM?

8.What is the Viterbi algorithm? How is it used?

9.Describe some approaches to decoding.

10.You are training a maximum entropy Markov model to identify biomedical entities. Identify \( \geq 5\) useful features for your MEMM.

11.Complete the code to fit a DictVectorizer using the given feature functions:

"The North Wind and the Sun had a quarrel about which of them was the stronger" \(\rightarrow\)

python [ 'startsWithUpper', 'token=North', 'token=Sun', 'token=The', 'token=Wind', 'token=a', 'token=about', 'token=and', 'token=had', 'token=of', 'token=quarrel', 'token=stronger', 'token=the', 'token=them', 'token=was', 'token=which' ]

12.Complete the code to calculate frequency of letters in the given input, and transform it into a vector using DictVectorizer.

## \( \text{input} \rightarrow \text{output} \)

- \(\text{c c a a} \rightarrow [[2. 0. 2.]] \)
- \(\text{a a c c} \rightarrow [[2. 0. 2.]] \)
- \(\text{a a a a} \rightarrow [[4. 0. 0.]] \)

13.What one feature is required in a left \(\rightarrow\) right MEMM?

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