CS4395 Homework 3 -Solved

Python and NLTK features to explore a text file and create a word guessing game 
Turn in: 
• Upload your .py code to eLearning for grading 
• Upload your .py code to your portfolio, and create a link to on your index page 
• This program should be written with an IDE so that it can be run interactively 
Instructions: 
1. Download the anat19.txt file from the GitHub and save it in the same folder as your Python 
program. The file is the text from one chapter of an anatomy textbook. Send the filename to the 
main program in a system argument. If no system arg is present, print an error message and exit 
the program. 
2. Read the input file as raw text. Calculate the lexical diversity of the tokenized text and output it, 
formatted to 2 decimal places. Lexical diversity is the number of unique tokens divided by the 
total number of tokens. Lexical diversity indicates the richness of vocabulary in a text. For 
example, a lexical diversity of 0.05 means that 5% of the words in a text are unique. See this 
notebook in the GitHub for an example: https://github.com/kjmazidi/NLP/blob/master/Part_2- 
Words/Chapter_05_words/5.1_Words1.ipynb 
3. Write a function to preprocess the raw text: 
a. tokenize the lower-case raw text, reduce the tokens to only those that are alpha, not in 
the NLTK stopword list, and have length > 5 
b. lemmatize the tokens and use set() to make a list of unique lemmas 
c. do pos tagging on the unique lemmas and print the first 20 tagged items (see the 
pos_NLTK notebook in Part 2 Chapter 6 of the GitHub) 
d. create a list of only those lemmas that are nouns 
e. print the number of tokens (from step a) and the number of nouns (step d) 
f. return tokens (not unique tokens) from step a, and nouns from the function 
4. Make a dictionary of {noun:count of noun in tokens} items from the nouns and tokens lists; sort 
the dict by count and print the 50 most common words and their counts. Save these words to a 
list because they will be used in the guessing game. 
5. Make a guessing game function: 
a. give the user 5 points to start with; the game ends when their total score is negative, or 
they guess ‘!’ as a letter 
b. randomly choose one of the 50 words in the top 50 list (See the random numbers 
notebook in the Xtras folder of the GitHub) 
c. output to console an “underscore space” for each letter in the word 
d. ask the user for a letterCS 4395 Intro to NLP 
Dr. Karen Mazidi 
Caution: All course work is run through plagiarism detection software comparing 
students’ work as well as work from previous semesters and other sources. 
e. if the letter is in the word, print ‘Right!’, fill in all matching letter _ with the letter and 
add 1 point to their score 
f. if the letter is not in the word, subtract 1 from their score, print ‘Sorry, guess again’ 
g. guessing for a word ends if the user guesses the word or has a negative score 
h. keep a cumulative total score and end the game if it is negative (or the user entered ‘!’) 
for a guess 
i. right or wrong, give user feedback on their score for this word after each guess 
6. Create a link to this document on your index page 
Note: Feel free to make tweaks to the game to customize it, as long as it has the basic functionality. In 
other words, you can make a better version of this game if you want. 
Sample run of the game: 
Let's play a word guessing game! 
_ _ _ _ _ _ 
Guess a letter:e 
Right! Score is 6 
_ e _ _ e _ 
Guess a letter:a 
Sorry, guess again. Score is 5 
_ e _ _ e _ 
Guess a letter:i 
Sorry, guess again. Score is 4 
_ e _ _ e _ 
Guess a letter:u 
Sorry, guess again. Score is 3 
_ e _ _ e _ 
Guess a letter:o 
Sorry, guess again. Score is 2 
_ e _ _ e _ 
Guess a letter:t 
Sorry, guess again. Score is 1 
_ e _ _ e _ 
Guess a letter:s 
Right! Score is 2 
_ e s s e _ 
Guess a letter:m 
Sorry, guess again. Score is 1 
_ e s s e _ 
Guess a letter:l 
Right! Score is 2 
_ e s s e l 
Guess a letter:v 
Right! Score is 3 
v e s s e l 
You solved it! 
_ _