In this task, you need to provide the parts-of-speech tag of a word present in a sentence specified within curly braces  ( '{{ ... }}' ). The parts-of-speech tags are coarse labels that represent a category of words with similar grammatical properties. The list of part-of-speech tags i.e tagset of this corpus is - 
 '.': Period symbol is used for symbols denoting Punctuations/Separations such as comma, period, backticks etc., 
 'ADJ': Adjectives are words that typically modify nouns and specify their properties or attributes, 
 'ADP': Adposition is a cover term for prepositions and postpositions, 
 'ADV': Adverbs are words that typically modify verbs for such categories as time, place, direction or manner, 
 'CONJ': A word used to connect clauses or sentences or to coordinate words in the same clause, 
 'DET': Determiners are words that modify nouns or noun phrases and express the reference of the noun phrase in context, 
 'NOUN': Nouns are a part of speech typically denoting a person, place, thing, animal or idea, 
 'NUM': A numeral is a word, functioning most typically as a determiner, adjective or pronoun, that expresses a number and a relation to the number, such as quantity, sequence, frequency or fraction, 
 'PRT': Particles are function words that must be associated with another word or phrase to impart meaning and that do not satisfy definitions of other universal parts of speech, 
 'PRON': Pronouns are words that substitute for nouns or noun phrases, whose meaning is recoverable from the linguistic or extralinguistic context, 
 'PROPN': A proper noun is a noun (or nominal content word) that is the name (or part of the name) of a specific individual, place, or object, 
 'VERB': A verb is a member of the syntactic class of words that typically signal events and actions, can constitute a minimal predicate in a clause, and govern the number and types of other constituents which may occur in the clause, 
 'X': The tag X is used for words that for some reason cannot be assigned a real part-of-speech category.

Example Input: Sentence: This was not {{ a }} particularly shocking or unexpected thing -- it was previously known to her that it might happen . 
Word: a
Example Output: DET

Example Input: Sentence: It is interesting that a {{ 1 }} : 1 correspondence can be established between the lines of two such pencils , so that in a sense a unique image can actually be assigned to each tangent . 
Word: 1
Example Output: NUM

Example Input: Sentence: He knew {{ who }} was riding after him -- the men he had known all his life , the men who had worked for him , sworn their loyalty to him . 
Word: who
Example Output:
PRON