Definition: 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 fine 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 : '$': Dollar Sign, "''": Single Quotes, ',': Comma Symbol, '-LRB-': Left Parantheses, '-RRB-': Right Parantheses, '.': Period, ':': Colon, 'ADD': Email Address, 'AFX': Affix, 'CC': Coordinating conjunction, 'CD': Cardinal Number, 'DT': Determiner, 'EX': Existential there, 'FW': Foreign Word, 'GW': Go with, 'HYPH': Hyphen symbol, 'IN': Preposition or a subordinating conjunction, 'JJ': Adjective, 'JJR': A comparative Adjective, 'JJS': A Superlative Adjective, 'LS': List item Marker, 'MD': Modal, 'NFP': Superfluous punctuation, 'NN': Singular Noun, 'NNP': Singular Proper Noun, 'NNPS': Prural Proper Noun, 'NNS': Prural Noun, 'PDT': Pre-determiner, 'POS': Possessive Ending, 'PRP': Personal pronoun, 'PRP$': Possessive Pronoun, 'RB': Adverb, 'RBR': Comparative Adverb, 'RBS': Superlative Adverb, 'RP': Particle, 'SYM': Symbol, 'TO': To , 'UH': Interjection, 'VB': Base form Verb, 'VBD': Verb in Past tense, 'VBG': Verb in present participle, 'VBN': Verb in past participle, 'VBP': Verb in non-3rd person singular present, 'VBZ': Verb in 3rd person singular present, 'WDT': Wh-determiner, 'WP': Wh-pronoun, 'WP$' Possessive Wh-pronoun, 'WRB': Wh-adverb, 'XX': Unknown, '``': Double backticks.

Positive Example 1 -
Input: Sentence: Those things ended up being a windsheild washer fluid tank {{ ( }} 1 screw ) and the air filter canister ( 4 spring clips ) . 
Word: (
Output: -LRB-

Positive Example 2 -
Input: Sentence: I melt brass and cast it {{ http://www.mikegigi.com/castgobl.htm }} 
Word: http://www.mikegigi.com/castgobl.htm
Output: ADD

Negative Example 1 -
Input: Sentence: It s between sea {{ and }} mountains so you can camp on beach or between mountains ! 
Word: and
Output: VB

Now complete the following example -
Input: Sentence: the food was horrible not cooked like it should be , they got the order wrong on a number of {{ occasions }} , and once forgot about my order . 
Word: occasions
Output: 


Answer: NNS