Bigram-and-Trigram-Modeling

Find the code in Tag (Named Bigram)

Took the shared "Unigram Modeling.IPYNB" as the starting point for this task.

It builds a unigram language model.

And build the bigram and the trigram language models.

Took a corpus realdonaldtrump.csv. Take the tweets out. Remove any links and did the following:

1) Split data into 80% training and 20% test sets.

2) Remove punctuation marks.

3) Remove the newline characters.

4) Remove stopwords.

5) Use the n-grams module from nltk.util package to build bigrams and trigrams.

6) Find the frequencies of bigrams and trigrams in the corpus.

Tabulate them and show the histogram. Compare it with Zipf's Law.

7) Do Add-1 smoothing (Laplace) as explained in the slides.

8) Build a dictionary showing bigram and trigram probabilities against each sorted in increasing order.

9) Retabulate the reconstituted counts for the bigrams and the trigrams.

10) Redo the histogram and compare it again with Zipf's Law.

11) Take a four-word string as the test sample and find both its unigram, bigram, and trigram perplexities.

12) Natural Langauge Generation Task: Based on step 7, build CDF (Cumulative Distribution Function).

Generate a random number and compare it with the CDF.

Pick the word whose CDF value is closest to it and return it as the generated text.

13) Build the Shannon game as discussed in the class.

Predict the last word (hide it during prediction time) of the tweets in the test set based on one word before

(Bigram modeling), and two words before (trigram modeling), and evaluate the results using accuracy.