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Being familiar with Snap!This task will help you to learn about the programming environment. You can explore it by yourself under the guidance of reference manuals. For example, please try to select the “average” item under the “Variables” category and see what happened in the Stage region. Task 2. NLP training using word embeddingWord embedding is a popular method to translate the human language into a language that machines can be recognized and processed. It first constructs a set of features from the text for each word, and then do distributed representations of that set of features. Here, we have provided training text which placing the word in a 300-dimensional space (i.e., the word will be reported in a list of 300 numbers).Task 2-1. Generate a vector for "cat" and visualize itPlease combine some blocks to generate a vector for "cat" and visualize it. The followings are some necessary blocks and an example to generate the vector for a word. Show your program below.Task 2-2. Calculate the distance from "dog" to "cat"The distance between two words can be calculated using word embedding. The block below returns the distance between “flower” and “honey”. Please calculate the distance between “dog” and “cat”. Show your program below and give your answer.The distance between "dog" and "cat" is _____ (with two decimal places)Task 2-3. Find the closest word to "cat"Please combine the following blocks to find the closest word to “cat”. Show your program below and give your answer.The closest word to "cat" is _____.Task 2-4. Find the 5 closest words to "cat" Please combine the following blocks to find the 5 closest words to “cat”. Show your program below and give your answer.The 5 closest words to “cat” is (______,______,______,_______,_______)Task 2-5. Use the word embedding method to answer which of the following words is closer to “mouse”.Please review the blocks you’ve learned, try to answer which words is closer to “mouse”. Show your program below and give your answer. A:“fox” B:“cow” C:“lion”The closest word to “mouse” is ______ (A or B or C).Task 2-6. Modify the program to calculate the distance between “man” and “king”Vectors can be added and subtracted, please modify the program to calculate the distance between “man” and “king” and give your answer.Task 2-7. Use the word embedding method to compare the relationships between word groups.Please try to compare the relationship between word groups. Show your program below and give your answer to which two groups of words are closer. A:“king-queen” and “man-woman” B:“king -woman” and “queen - man” C:“man-king” and “wowan-queen” The followings are some blocks you might need.The closer word groups is ____ (A or B or C). reportCONSSlow but elegant way of computing distance. "square of distance from features ..." is much faster.

Reports a list created by applying reporter to corresponding elements of the lists.

undefined11undefinedReports the word that is closest to the features. You can choose from among 15 languages or leave it blank for the default language. There is a full-featured version of this block as well.

Chinese English Finnish French German Greek Hindi Indonesian Italian Japanese Lithuanian Portuguese Sinhalese Spanish SwedishReports all the words sorted by closeness to 'features'. Optionally can report pairs of words and distances. Distances are measured using cosine similarity.

Chinese English Finnish French German Greek Hindi Indonesian Italian Japanese Lithuanian Portuguese Sinhalese Spanish Swedishfalsewordsundefined

record_callbacks(finished_callback);false

undefinedsets the value of an existing property or inserts it anew

widthtype of data width height backColorRed backColorGreen backColorBlue leftOffset upperOffset lowerOffset title titleHeight xLabel xLabelHeight yLabel yLabelHeight xLeft xRight yLower yUpper lineStyle lineWidth lineColorRed lineColorGreen lineColorBlue datapointStyle datapointWidth datapointConnected datapointColorRed datapointColorGreen datapointColorBlue scalesPrecision scalesTextheight scalesNumberXintervals minValue maxValue columns rowsnametitleHeightnamexLabelHeightnameyLabelHeightnamescalesTextheightreads the value of an existing property

widthtype of data width height backColorRed backColorGreen backColorBlue leftOffset upperOffset lowerOffset title titleHeight xLabel xLabelHeight yLabel yLabelHeight xLeft xRight yLower yUpper lineStyle lineWidth lineColorRed lineColorGreen lineColorBlue datapointStyle datapointWidth datapointConnected datapointColorRed datapointColorGreen datapointColorBlue scalesPrecision scalesTextheight scalesNumberXintervals minValue maxValue columns rowssets the distances of the diagram axes from the edges of the DataSprite

xLabelundefined0yLabelundefined0

1undefined

#11datacountmapmany##1undefined#undefined1data listscountundefinedDraws the vector as a vertical line for each number.

undefinedReturns only after eCraft2Learn library is loaded

Should only be called by Load eCraft2Learn library

Reports a list of 300 numbers that "describes" the word. You can choose among 13 languages or leave it blank and use the default language. To learn about these numbers see https://en.wikipedia.org/wiki/Word_embedding

Chinese English Finnish French German Greek Hindi Indonesian Italian Japanese Lithuanian Portuguese Sinhalese Spanish Swedish

Chinese English Finnish French German Greek Hindi Indonesian Italian Japanese Portuguese Sinhalese Swedishundefined

Chinese English Finnish French German Greek Hindi Indonesian Italian Japanese Portuguese Sinhalese Swedish

de:für jedes _ von _ _

0