Adaface Sample Natural Language Processing Questions

Consider the following pseudo code for calculating the probability of a sentence using a bigram language model:

Assume that the bigram and unigram counts are as follows:
            
            bigram_counts = {("i", "like"): 2, ("like", "cats"): 1, ("cats", "too"): 1}
            unigram_counts = {"i": 2, "like": 2, "cats": 2, "too": 1}
            vocabulary_size = 4
            
            What is the probability of the sentence "I like cats too" using the bigram language model?

You are working on a natural language processing project and need to preprocess the text data for further analysis. Your task is to tokenize the text and apply stemming to the tokens. Assuming you have an English text corpus, which of the following combinations of tokenizer and stemmer would most likely result in the best balance between token granularity and generalization?