Please don't provide answers that are given by an AI chatbot.

Question $1$:

Multiple$-$Answer Question:

Why is smoothing so important for n$-$gram modeling? Please select all that apply.

A$.$ All smoothing methods "steal from the rich to give to the poor", however, add$-$one smoothing steals way too much.

B$.$ Since MLE underfits the training data, smoothing methods reassign probability mass from observed to unobserved events to avoid overfitting$/$zero probabilities.

C$.$ Smoothing is the process of flattening a probability distribution implied by a language model so that all reasonable word sequences can occur with some probability.

D$.$ Smoothing is an intuitively simple concept. It pretends each $n-$gram occurs once more than it actually does in the training data set.

E$.$ If an $N-$gram is never observed in the training data, it cannot occur in the evaluation data set.

Question $2$:

Which one of the following languages over the alphabet $\{$a$,$b$\}$ is described by the regular expression: $($a$+$b$)*$a$($a$+$b$)*$a$($a$+$b$)*$

A$.$ The set of all strings containing the substring aa$.$

B$.$ The set of all strings that begin and end with either $a$ or $b.$

C$.$ The set of all strings containing at most two aa's.

D$.$ The set of all strings containing at least two aa's.

Question $3$:

How many trigrams phrases can be generated from the sentence$-$Natural$\_$language$\_$processing is technology that allows @computer systems to #analyze and #understand normal human$-$language."

$--$after performing two text cleaning steps:

$1.$ Stopword Removal

$2.$ Replacing punctuations by a single space

A$.12$

B$.10$

C$.9$

D$.11$