How can you use NLTK to extract the most probable next word for text prediction tasks

Question

With the help of a code snippet, can you explain How can you use NLTK to extract the most probable next word for text prediction tasks?

Answer 1

To extract the most probable next word for text prediction tasks using NLTK, you can use an N-gram model. By training a bigram or trigram model, you can calculate the probability of the next word based on the previous word(s). Here is the code snippet showing how:

In the above code snippet, we are using the following techniques:

• Tokenize the Text: Break the text into tokens using word_tokenize.
• Create N-grams: Generate bigrams or trigrams using ngrams.
• Frequency Distribution: Calculate the frequency of these N-grams to understand the probability of word pairs.
• Prediction: Given a word (e.g., 'learning'), predict the next word by checking which word most frequently follows it in the corpus.

Hence, this simple N-gram model can predict the most probable next word based on historical data, but more advanced models like LSTM or Transformer-based models offer improved accuracy and context-awareness.