NLG Defined

Natural Language Generation is a subset of artificial intelligence (AI) dedicated to producing text outputs that are naturally readable from structured data sources. In contrast to Natural Language Processing (NLP), which focuses on decoding and comprehending human language, NLG is centered on the creation of coherent and contextually appropriate text.

Typically, NLG systems take in structured data—like templates, rules, or statistical models—and convert this information into human-readable language. The complexity of these systems can vary, from straightforward rule-based methods to advanced machine learning frameworks capable of recognizing patterns within extensive datasets.

Python: A Center for NLG Innovation

With its extensive array of libraries and frameworks, Python stands out as an excellent platform for developing NLG applications. Whether the goal is basic text generation or sophisticated language modeling, Python equips developers with the necessary tools to implement a variety of NLG techniques.

To demonstrate the capabilities of Python in NLG, let's explore a practical example utilizing the well-known NLTK (Natural Language Toolkit) library.

import nltk

from nltk.tokenize import word_tokenize

from nltk.corpus import reuters

from nltk.corpus import brown

from nltk import trigrams

import random

def generate_text():

corpus = reuters.words() + brown.words()

trigram_model = {}

for sentence in corpus:

words = ['', ''] + word_tokenize(sentence.lower()) + ['']

for w1, w2, w3 in trigrams(words):

key = (w1, w2)

if key in trigram_model:

trigram_model[key].append(w3)

else:

trigram_model[key] = [w3]

text = ['', '']

while text[-1] != '':

key = tuple(text[-2:])

if key in trigram_model:

text.append(random.choice(trigram_model[key]))

else:

break

generated_text = ' '.join(text[2:-1])

print(generated_text)

# Example usage

generate_text()