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Q 1 : The following sentence is partially tagged with POS Tags, where bear can be either a VB ( verb ) or a NN

Q1: The following sentence is partially tagged with POS Tags, where "bear" can be either a VB (verb) or a NN (noun):
Your/Which formulas can calculate the most probable tag sequence for "bear"?
1) P(bear|NN)* P(NN|VB)*P(VB|NN)
2) P(bear|VB)*P(VB|MD)*P(NN|VB)
3) P(will|MD)*P(bear|VB)* P(fruit|NN)
4) P(NN|NN)* P(NN|MD)*P(bear|NN)
5) P(bear|VB)*P(VB|NN)*P(VB|MD)PPR$ efforts/NN will/MD bear/? fruit/NN.
A.2,4
B.1,5
C.3,5
D.1,2
E.4,5
Q2: The following sentence is partially tagged with POS Tags, where "race" can be either a VB (verb) or a NN (noun):
Secretariat/NNP is/VBZ expected/VBN to/TO race/? tomorrow/NR.
Which formulas can calculate the most probable tag sequence for "race"?
Q3: What is cosine similarity? How to calculate it?
Q4: TF-IDF helps to establish how important a particular word is in the context of the document corpus. TF-IDF takes into account the number of times the word appears in the document and is offset by the number of documents that appear in the corpus.
TF is the frequency of terms divided by the total number of terms in the document.
IDF is obtained by dividing the total number of documents by the number of documents containing the term and then taking the logarithm of that quotient.
TF-IDF is then the multiplication of two values TF and IDF.
Suppose that we have term count tables of a corpus consisting of only two documents, as in the picture (table):
Calcuate TF-IDFs for the term example for Document 1 and Document 2, respectively.
Q5: Describe Yarowsky's (1995) technique for word sense disambiguation and illustrate how it would disambiguate the following two senses of "sake":
Sense 1: sake, interest (a reason for wanting something done: "for your sake", "died for the sake of his country")
Sense 2: sake, saki, rice beer (Japanese alcoholic beverage made from fermented rice, usually served hot)
Q6: Suppose you want to develop a new approach to summarization that extracts phrases rather than full sentences and puts together the phrases to form a sentence for the summary. Many summarization systems use language models. Please clearly explain your algorithm. You can draw a diagram of the summarization system architecture to help you answer this question.

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