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 create an N-gram model (e.g., bigrams or trigrams) to calculate the likelihood of a word based on its preceding word(s). Here is the code below you can refer to:

In the above code, we are using the following steps:

• Tokenize the Text: Tokenize the text into individual words.
• Create N-grams: Use NLTK's ngrams function to create bigrams or trigrams.
• Frequency Distribution: Calculate the frequency of these N-grams using FreqDist.
• Prediction: Given a word (e.g., 'learning'), predict the next word by finding which word frequently follows it.

Hence, this approach uses a basic bigram model, which can be expanded with higher-order N-grams or other advanced models for improved predictions.

Answer 2

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.