Instructions Please submit your solution by the beginning of the week 9 lecture (Nov 30). Submissions should be made on gradescope. Please complete homework individually. Download the Assignment 1 data from the course webpage: http://cseweb.ucsd.edu/classes/fa20/ cse258-a/files/assignment1.tar.gz. We will use the reviews in train Category.json.gz. Code is provided on the course webpage (week5.py) and in the lecture notebook showing how to load and perform simple processing on the data. Executing the code requires a working install of Python 3.0 with the scipy packages installed. Tasks Using the code provided on the webpage, read the first 10,000 reviews from the corpus, and read the reviews without capitalization or punctuation. 1. How many unique bigrams are there amongst the reviews? List the 5 most-frequently-occurring bigrams along with their number of occurrences in the corpus (1 mark). 2. The code provided performs least squares using the 1000 most common unigrams. Adapt it to use the 1000 most common bigrams and report the MSE obtained using the new predictor (use bigrams only, i.e., not unigrams+bigrams) (1 mark). Note that the code performs regularized regression with a regularization parameter of 1.0. The prediction target should be log2 (hours + 1) (i.e., our transformed time variable). 3. Repeat the above experiment using unigrams and bigrams, still considering the 1000 most common. That is, your model will still use 1000 features (plus an offset), but those 1000 features will be some combination of unigrams and bigrams. Report the MSE obtained using the new predictor (1 mark). 4. What is the inverse document frequency of the words ‘destiny’, ‘annoying’, ‘likeable’, ‘chapter’, and ‘interesting’? What are their tf-idf scores in review ID r75487422 (using log base 10, unigrams only, following the first definition of tf-idf given in the slides) (1 mark)? 5. Adapt your unigram model to use the tfidf scores of words, rather than a bag-of-words representation. That is, rather than your features containing the word counts for the 1000 most common unigrams, it should contain tfidf scores for the 1000 most common unigrams. Report the MSE of this new model (1 mark). 6. Which other review has the highest cosine similarity compared to review ID r75487422, in terms of their tf-idf representations (considering unigrams only). Provide the reviewID, or the text of the review (1 mark)? 7. Implement a validation pipeline for this same data, by randomly shuffling the data, using 10,000 reviews for training, another 10,000 for validation, and another 10,000 for testing.1 Consider regularization parameters in the range {0.01, 0.1, 1, 10, 100}, and report MSEs on the test set for the model that performs best on the validation set. Using this pipeline, compare the following alternatives in terms of their performance (all using 1,000 dimensional word features): • Unigrams vs. bigrams • Removing punctuation vs. preserving it. The model that preserves punctuation should treat punctuation characters as separate words, e.g. “Amazing!” would become [‘amazing’, ‘!’] • tfidf scores vs. word counts In total you should compare 2 × 2 × 2 × 5 = 40 models (8 models and 5 regularization parameters), and produce a table comparing their performance (2 marks)