How can you use TF-IDF values from NLTK for generative text ranking

Question

Can you tell me how I can use TF-IDF values from NLTK for generative text ranking?

Answer 1

To use TF-IDF values from NLTK for generative text ranking, you can compute the TF-IDF of words in your corpus and then rank sentences or generated text based on their relevance to a target query or context. Here is the code snippet which you can refer to:

In the above code, we are using the following:

• TF-IDF Vectorizer: The TfidfVectorizer from sklearn computes TF-IDF values for the corpus.
• Cosine Similarity: The cosine_similarity function is used to compare the query's TF-IDF representation with the corpus to calculate similarity scores.
• Ranking: Sentences from the corpus are ranked based on their similarity to the query.

The output of the above code would be:

Hence, this ranks the sentences based on how similar they are to the query, which can be useful for ranking generated text in a relevant way based on a given context.