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What is Natural Language Generation?

Natural Language Generation (NLG) involves the process of generating coherent and meaningful human-like text from structured data or inputs. It is a branch of artificial intelligence that focuses on transforming data into natural language, facilitating communication between humans and machines. NLG plays a crucial role in various applications, from automated content creation to chatbots and virtual assistants.

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NLG: Creating Text Sequences with NLTK

import nltk from nltk.book import text1 # Tokenize the text tokens = nltk.word_tokenize(" ".join(text1.tokens)) # Build a bigram model bigrams = list(nltk.bigrams(tokens)) # Generate text based on the bigram model seed_word = "good" generated_text = [seed_word] for _ in range(20): next_word = None for bg in bigrams: if bg[0] == seed_word: next_word = bg[1] break if next_word is None: break generated_text.append(next_word) seed_word = next_word print("Generated Text:") print(" ".join(generated_text))

Explanation:

• This code snippet demonstrates a basic text generation approach using NLTK.
• It first tokenizes a sample text (here, using NLTK's text1 from the NLTK book).
• It then constructs a bigram model (where each word is predicted based on its preceding word).
• Finally, it generates a sequence of words starting from a seed word ("good" in this case) based on the bigram model.

This example provides a foundational understanding of how NLG can be implemented using simple statistical models like bigrams. Students can experiment with different seed words and texts to observe how the generated text varies based on the initial conditions and input data.

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