Write a bare-bones Markov text generator.
Implement a function of the form finish_sentence(sentence, n, corpus, deterministic=False) that takes four arguments:
1. a sentence [list of tokens] that weāre trying to build on,
2. n [int], the length of n-grams to use for prediction, and
3. a source corpus [list of tokens]
4. a ļ¬ag indicating whether the process should be deterministic [bool]
and returns an extended sentence until the ļ¬rst ., ?, or ! is found OR until it has 15 total tokens.
If the input ļ¬ag deterministic is true, choose at each step the single most probable next token. When two tokens are equally probable, choose the one that occurs ļ¬rst in the corpus.
If deterministic is false, draw the next word randomly from the appropriate distribution. Use stupid backoļ¬ and no smoothing.
Provide some example applications of your function in both deterministic and stochastic modes, for a few sets of seed words and a few diļ¬erent n.
As one (simple) test case, use the following inputs:
sentence = [āsheā, āwasā, ānotā] n = 3 corpus = [
w.lower for w in nltk.corpus.gutenberg.words(āausten-sense.txtā)
] deterministic = True
The result should be [āsheā, āwasā, ānotā, āinā, ātheā, āworldā, ā.ā]
Add your method to a ļ¬le/module named mtg.py and use the test script text_mtg.py to verify that this example works.
You should turn in a document (.txt, .md, or .pdf) answering all of the red items above. You should also turn in Python scripts (.py) for each of the blue items.
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