According To Set Theory, Sets That Do NOT Contain Themselves Are Typically Referred To As

⟩ According to Set Theory, sets that do NOT contain themselves are typically referred to as

normal sets

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What is the major difference between CRF (Conditional Random Field) and HMM (Hidden Markov Model)? A) CRF is Generative whereas HMM is Discriminative model B) CRF is Discriminative whereas HMM is Generative model C) Both CRF and HMM are Generative model D) Both CRF and HMM are Discriminative model

In a corpus of N documents, one document is randomly picked. The document contains a total of T terms and the term “data” appears K times. What is the correct value for the product of TF (term frequency) and IDF (inverse-document-frequency), if the term “data” appears in approximately one-third of the total documents? A) KT * Log(3) B) K * Log(3) / T C) T * Log(3) / K D) Log(3) / KT

How many trigrams phrases can be generated from the following sentence, after performing following text cleaning steps Stopword Removal Replacing punctuations by a single space “#Analytics-vidhya is a great source to learn @data_science.” A) 3 B) 4 C) 5 D) 6 E) 7

Google Search’s feature – “Did you mean”, is a mixture of different techniques. Which of the following techniques are likely to be ingredients? Collaborative Filtering model to detect similar user behaviors (queries) Model that checks for Levenshtein distance among the dictionary terms Translation of sentences into multiple languages A) 1 B) 2 C) 1, 2 D) 1, 2, 3

Solve the equation according to the sentence “I am planning to visit New Delhi to attend Analytics Vidhya Delhi Hackathon”. A = (# of words with Noun as the part of speech tag) B = (# of words with Verb as the part of speech tag) C = (# of words with frequency count greater than one) What are the correct values of A, B, and C? A) 5, 5, 2 B) 5, 5, 0 C) 7, 5, 1 D) 7, 4, 2 E) 6, 4, 3

Polysemy is defined as the coexistence of multiple meanings for a word or phrase in a text object. Which of the following models is likely the best choice to correct this problem? A) Random Forest Classifier B) Convolutional Neural Networks C) Gradient Boosting D) All of these

While working with context extraction from a text data, you encountered two different sentences The tank is full of soldiers. The tank is full of nitrogen. Which of the following measures can be used to remove the problem of word sense disambiguation in the sentences? A) Compare the dictionary definition of an ambiguous word with the terms contained in its neighborhood B) Co-reference resolution in which one resolute the meaning of ambiguous word with the proper noun present in the previous sentence C) Use dependency parsing of sentence to understand the meanings

Which of the following statement is(are) true for Word2Vec model? A) The architecture of word2vec consists of only two layers – continuous bag of words and skip-gram model B) Continuous bag of word is a shallow neural network model C) Skip-gram is a deep neural network model D) Both CBOW and Skip-gram are deep neural network models E) All of the above

Which of the following regular expression can be used to identify date(s) present in the text object “The next meetup on data science will be held on 2017-09-21, previously it happened on 31/03, 2016” A) d{4}-d{2}-d{2} B) (19|20)d{2}-(0[1-9]|1[0-2])-[0-2][1-9] C) (19|20)d{2}-(0[1-9]|1[0-2])-([0-2][1-9]|3[0-1]) D) None of the above

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