Natural Language Processing Assessment Test This test comprises of 20 Questions and you stand a chance to avail upto 20% discount on Natural Language Processing Course basis your assessment score. GOOD LUCK! Which of the below data types is not supported by python? Like this:Like Loading... Tuple List Dataframe Factor Consider the following list: mylist = [“This”, “is”, “my”, “list”] What will the command “mylist[1]” return? Like this:Like Loading... “my” “list” “is” “this” If an outlier (value on a different scale than other values) is added to a data, it will affect the mean. True or false? Like this:Like Loading... True False Remove the outlier from the data set 73, 77, 81, 86, 900, 68, 28 and calculate the new Mean. Like this:Like Loading... 89 122 77 69 What is the difference between a bar graph and a histogram? Like this:Like Loading... Histogram doesn’t show the entire range Bar chart is symmetric whereas Histogram is always skewed Histograms are for continuous variables whereas Bar graphs are for categorical variables Histograms are for categorical variables whereas Bar graphs are for continuous variables Which of the following is the first stage of predictive modeling? Like this:Like Loading... Data Exploration Problem Definition Hypothesis Generation Model Building In a confusion matrix, the total number of actual positives are given as? Like this:Like Loading... True Positive + False Negative False Negative + True Negative False Positive + False Negative True Positive + False Positive What do we mean by the process of dimensionality reduction? Like this:Like Loading... Process of eliminating observations from the dataset Process of eliminating independent variables randomly Process of eliminating independent variables such that maximum information is retained with minimum variables None of the above Which of the following statements are true about the Naive Bayes algorithm? Like this:Like Loading... It calculates the probability of only 1 class It assumes that the features are completely independent of each other None of the above What exactly the term “Ensembling” stands for in predictive modelling? Like this:Like Loading... Combining different datasets for better predictions Combining different models for better predictions Combining independent variables to form new ones None of these What are the top 3 most frequent words in the text snippet below? “I am a huge football fan so the title of the repository instantly had my attention. Google Research and football – what in the world do these two have to do with each other? This repository contains a reinforcement learning environment based on the open-source game Gameplay Football.” Like this:Like Loading... “the”, “in”, “based” “in”, “so”, “my” “football”, “with”, “and” “the”, “repository”, “do” What are the top 3 most frequent words in the text snippet below, excluding the words “the” and “a”? “So, I was browsing YouTube a few weeks ago searching for a certain category to watch. That’s when my data scientist thought process kicked in. Could I extract data about the videos and build a model to classify them into their respective categories?” Like this:Like Loading... “few”, “into”, “my” “I”, “to”, “data” “for”, “data”, “in” “my”, “about”, “and” Given below is a list of terms coming from three different themes or topics. [“goal”, “sun”, “car”, ”space”, “football”, “midfielder”, “engine”, “galaxy”] Split this list into groups of similar word together. Select the right option. Like this:Like Loading... [“goal”, “sun”, “football”] [“midfielder”, “engine”, “galaxy”] [“car”, “space”] [ “sun”, “football”] [“midfielder”, “engine”, “galaxy”] [“car”, “space”, “goal”] [ “sun”, “football”, “galaxy”] [“midfielder”, “engine”] [“car”, “space”, “goal”] [“sun”, “space”,”galaxy”] [“goal”, “football”, “midfielder] [“car”, “ engine”] What is the right set of words to fill in the blanks in the text below? “Natural language processing (NLP) is a subfield _____ computer science, information engineering, and artificial intelligence concerned _____ the interactions between computers _____ human (natural) languages, in particular how to program computers to process and analyze large _____ of natural language data.” Like this:Like Loading... “is”, “with”, “not”, “size” “can”, “about”, “for”, “number” “of”, “with”, “and”, “amounts” None of the above Identify the name of the locations or places in the sentences below: 10 places you must visit in the Big Apple. Central Park is located in Manhattan. Rains hit flight operations in Mumbai Like this:Like Loading... 1- Big Apple 2- (Central Park, Manhattan) 3- Mumbai 1- places 2- Manhattan 3- Mumbai 1- (places, visit) 2- Central Park 3- Mumbai Identify the sentiment in the statement below: “Angry citizens sue government over growing air pollution“ Like this:Like Loading... Negative Positive Neutral Given below are three sample SMS’s. Spot the spam SMS Loan for $2000 is approved for you if you receive this SMS. 1 min verification & cash in 1 hr. Give a call at +1-341******* Sending you smiles for every moment of your special day…Have a wonderful time and a very happy birthday! Dear customer, we have refunded Rs. 500 to your Bank Account/Card for your failed payment. Like this:Like Loading... 1 and 2 2 3 1 Given below are a few questions from movies.stackexchange.com Is this character still alive at the end of Stranger Things season 2? What was the waiter’s dialogue in the movie? Why do movie directors use brown tint on Mexico cities? Assign suitable tags to each of these questions. You can use the following tags. Tags: “plot-explanation”, “character”, “dialogue”, “production” Like this:Like Loading... 1- “dialogue” 2- “character”, “production” 3- “plot-explanation” 1- “plot-explanation”, “character” 2- “dialogue” 3- “production” 1- “plot-explanation”, “character” 2- “dialogue” 3- “production” 1- “production” 2- “dialogue”, “plot-explanation” 3- “character” Consider the statement below and extract intent (action to be taken) and entities from it. “I would like to go to Los Angeles” Like this:Like Loading... Intent- see Entities- “Los Angeles” Intent- travel Entities- None Intent- travel Entities- “Los Angeles” What information can we extract from text? Like this:Like Loading... Word count and character count Dates, e-mails, and phone numbers Sentiment of the text All of the above Thank you for taking our survey. Like this:Like Loading... 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