NLP2020
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Explain the intuition behind distributional methods - that is, why do we believe that solving the task of predicting a word given its context yields embeddings which capture lexical semantics?
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Describe what is the Continuous Bag of Words (CBoW) model for learning word embeddings. List two ways in which the task modeled by CBoW is different from learning an n-gram language model.
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Describe three ways in which Pre-trained Transformer models are different from Neural Language Models
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ADJ ADP ADV CCONJ SCONJ NOUN PROPN PRON DET NUM VERB AUX PART PUNCT SYM INTJ X What would be the perplexity of the task of POS given a uniform distribution over the universal POS tagset?
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List 3 types of knowledge sources that can help in performing Parts of Speech tagging:
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Give an example word for each of the following classes: ADJ AUX PRON CCONJ ADV *my addition* think about each one from the classes: ADJ ADP ADV CCONJ SCONJ NOUN PROPN PRON DET NUM VERB AUX PART PUNCT SYM INTJ X
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Tag the following sentence in the format "John/PROPN eats/VERB an/DET apple/NOUN ./PUNCT" County officials in Maryland miscalculated how many ballots they would need on Election Day -- and quickly ran out in more than a dozen precincts .
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Give an example of word that can be tagged by using morphological clues.
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Give an example of sentence where an ambiguous word can be tagged using syntactic clues.
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Consider a distribution over two discrete variables x, y displayed in the following figure: Matrix view of 2 random variables. *in the original question there is a table of X/Y* Provide formulas to compute: Joint probability: Marginal probability for x: Conditional probability given y:
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