[Solved]Take Red Cone Put Green Block Square Take Blue Cone Red Circle Take Cube Red Circle Put Bl Q37252217

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take the red coneput the green block on the squaretake the blue cone on the red circletake the cube on the red circleput the blue cone on the red circle on the green circleput the block on the green circleput the blue block on the circleput the cone on the red square on the blocktake the cone on the squaretake the blockput the red cone on the squareput the block on the blue square on the squareput the green cone on the blue circleput the green cone on the red squareput the blue block on the red squareput the red block on the red squareput the red cone on the green circle on the squaretake the green cube on the red circletake the blue cube on the green circleput the block on the circle on the red squaretake the cubetake the blockput the block on the circle on the squaretake the green blocktake the cube on the red circleput the block on the red squareput the block on the square on the red circletake the cubeput the red cone on the squareput the cube on the circle on the red circleput the blue block on the blue squaretake the block on the squaretake the red blocktake the red cube on the squareput the cone on the red square on the red squareput the block on the red circletake the green blockput the block on the red circleput the blue cube on the red squareput the blue block on the blue squaretake the coneput the cone on the red circle on the squaretake the green blockput the cone on the square on the red squareput the cube on the square on the circletake the block on the red circletake the blockput the red cube on the red squaretake the blue blocktake the blocktake the green blockput the blue cube on the squareput the block on the square on the blue circleput the cube on the red circle on the red squaretake the red blockput the blue cube on the green circleput the red block on the blue circleput the cube on the squareput the blue cone on the square on the squareput the cone on the red circle on the squareput the blue block on the red circleput the cube on the red circle on the circletake the cubeput the blue cone on the circletake the coneput the blue cone on the red circle on the squareput the red cube on the red squareput the block on the blue circletake the cube on the circleput the cube on the circle on the circleput the block on the red squaretake the block on the circleput the green block on the green squaretake the red blocktake the coneput the cube on the squaretake the red coneput the cone on the square on the red circleput the red cone on the circleput the cone on the circle on the red circletake the red block on the blue squaretake the red blocktake the blockput the red block on the green circleput the block on the circle on the circleput the cone on the circle on the red circleput the red block on the circleput the cone on the squareput the block on the blue square on the squareput the cube on the red circletake the red blockput the red block on the circle on the blue squareput the cube on the red square on the circleput the blue cone on the blue circleput the block on the squareput the cube on the circletake the green blockput the red cone on the square on the circleput the block on the red circleput the blue cone on the red squareput the red cone on the circleput the blue cone on the red circle on the circletake the blue coneput the red block on the green circleput the green block on the red squareput the cube on the green squaretake the blue block (trainnig set)…………………..take the cone on the red blockput the block on the green circleput the blue cube on the square (test set)

0.0 other smoohing mothods of your che and bigram 20 5.0 the green reen one Probabs 1itles using the Unigzan mode1 Ptake the block on the green cirele.002 Birra: take the green eone on the eube other smoohing mothods of your cho Lab Assignment: N-gram Analysis An N-gram probability is the conditional probability of a word given the previous N-1 words. In this lab assignment, you will get practically acquainted with n-gram models, one of the simplest and yet most useful tools in the language technologist’s toolbox You will write your own programs to extract n-gram statistics from text and to use these statistics for language modeling and generation. 1. Extracting n-grams from text Your first task is to extract the frequencies of all word unigrams and bigrams found in the given corpus (the training set). Make sure to tokenize the texts in a reasonable way, treating words and punctuation marks as separate tokens. Sentence segmentation is optional but recommended for the following tasks. Based on the given test set, generate the probabilities of the sentences based on the unigram and bigram models. Output example: (sort based on word frequency) 100 20 the green cone 0.3 0.04 0.001 the green 100 green cone 20 0.25 0.15 Probabilities using the Unigram model: P (take the block on the green circle)0.002 Probabilities using the Bigram model: 2. Generating (almost completely) random text Your second task is to write a program that reads in n-grams extracted by the first program and uses them to generate sentences as random sequences of words constrained by the n-gram model. Remember the Shannon method we learnt in class? Both the sentences for unigram and the bigram model should be generated. The length of the generated text should be at least 5 words. Compare texts generated by the unigram and bigram, respectively. Which one resembles real text most? Can you think of ways quality of the text? Be creative! in which you could improve the Output example: Random sentences generated: Unigram: put on the block blue Bigram: take the green cone on the cube 3. Language Modelling and Smoothing Your third task is to write a program that reads in n-gram statistics and uses them to estimate the probability of new sentences. This will require some form of smoothing, and you may do this by implementing simple additive (Laplacian) smoothing or any other smoothing methods of your choice. In order to evaluate your language models, use the training set to estimate the probabilities and the test set for the evaluation (as given in your Ifolio/Google Classroom). Compare the results obtained with unigram and bigram models of the chosen model. Similar output like question (1) should be produced. Report and presentation Write a short report (around 2-3 pages) describing how you solved the three tasks. .For the first task, the report should contain the results of both models. .For the second task, the report should contain samples of text generated by the different models. .For the third task, the report should contain results generated after applying the chosen smoothing algorithm. Show transcribed image text 0.0 other smoohing mothods of your che
and bigram 20 5.0 the green reen one Probabs 1itles using the Unigzan mode1 Ptake the block on the green cirele.002 Birra: take the green eone on the eube other smoohing mothods of your cho
Lab Assignment: N-gram Analysis An N-gram probability is the conditional probability of a word given the previous N-1 words. In this lab assignment, you will get practically acquainted with n-gram models, one of the simplest and yet most useful tools in the language technologist’s toolbox You will write your own programs to extract n-gram statistics from text and to use these statistics for language modeling and generation. 1. Extracting n-grams from text Your first task is to extract the frequencies of all word unigrams and bigrams found in the given corpus (the training set). Make sure to tokenize the texts in a reasonable way, treating words and punctuation marks as separate tokens. Sentence segmentation is optional but recommended for the following tasks. Based on the given test set, generate the probabilities of the sentences based on the unigram and bigram models. Output example: (sort based on word frequency) 100 20 the green cone 0.3 0.04 0.001 the green 100 green cone 20 0.25 0.15 Probabilities using the Unigram model: P (take the block on the green circle)0.002 Probabilities using the Bigram model: 2. Generating (almost completely) random text Your second task is to write a program that reads in n-grams extracted by the first program and uses them to generate sentences as random sequences of words constrained by the n-gram model. Remember the Shannon method we learnt in class? Both the sentences for unigram and the bigram model should be generated. The length of the generated text should be at least 5 words. Compare texts generated by the unigram and bigram, respectively. Which one resembles real text most? Can you think of ways quality of the text? Be creative! in which you could improve the
Output example: Random sentences generated: Unigram: put on the block blue Bigram: take the green cone on the cube 3. Language Modelling and Smoothing Your third task is to write a program that reads in n-gram statistics and uses them to estimate the probability of new sentences. This will require some form of smoothing, and you may do this by implementing simple additive (Laplacian) smoothing or any other smoothing methods of your choice. In order to evaluate your language models, use the training set to estimate the probabilities and the test set for the evaluation (as given in your Ifolio/Google Classroom). Compare the results obtained with unigram and bigram models of the chosen model. Similar output like question (1) should be produced. Report and presentation Write a short report (around 2-3 pages) describing how you solved the three tasks. .For the first task, the report should contain the results of both models. .For the second task, the report should contain samples of text generated by the different models. .For the third task, the report should contain results generated after applying the chosen smoothing algorithm.

Expert Answer

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Answer to take the red cone put the green block on the square take the blue cone on the red circle take the cube on the red circle… . . .