Search⌘ K
Home
Courses
Generative AI Essentials

The Emergence of NLP

Learn how NLP evolved from rule-based systems to data-driven methods that power modern generative AI.

We'll cover the following...

What is natural language processing?

Generative AI may feel futuristic, but its creativity in writing, problem-solving, and conversation is already here. None of this would exist without natural language processing (NLP), the field that teaches machines to read, parse, and interpret human language. Before an AI can generate poetry, code, or art, it must first learn to understand language, making NLP the foundation of every breakthrough in generative technology.

Natural Language Processing (NLP) is the branch of artificial intelligence that enables computers to understand, interpret, and generate human language. It combines linguistics, computer science, and machine learning to help machines make sense of text and speech: from recognizing grammatical structure to extracting meaning and intent.

This lesson traces NLP’s journey from simple rule-based systems to methods like bag of words, TF-IDF, n-grams, and word embeddings. Each step forward, from rules to statistics to deep learning, was guided by one key question: How can machines truly understand language?

These breakthroughs paved the way for today’s large language models. By building on decades of progress, they now bring us closer to the idea of Artificial General Intelligence (AGI).

How did computers first interpret the text?

Early NLP relied on rule-based systems, where linguists and developers wrote detailed if-then instructions for every grammar quirk. Computers scanned text, matched it against these rules, and produced outputs that worked only in narrow cases. These systems could manage small tasks, like checking subject-verb agreement, but they were rigid and brittle. A new phrase or unusual wording would often break them, highlighting the need for models that could learn from data instead of fixed rules.

For example, a system might correctly change “he is” to “he’s” but fail completely on “he really is,” producing something awkward like “he really’s.”

Educative byte: One of the earliest examples of rule-based NLP is ELIZA, developed in the 1960s by Joseph Weizenbaum. ELIZA could mimic a psychotherapist by following scripted patterns, showcasing the potential and limitations of rule-based systems.

Early NLP relied on rigid rule-based systems, but encoding every language rule quickly became unmanageable. The shift to data-driven methods allowed machines to learn from word counts and patterns, paving the way for modern statistical approaches.

What is a bag of words?

Bag of Words (BoW) is one of the earliest statistical NLP methods, popular since the 1960s for text classification and search. Instead of rules, it counts how often each word appears, ignoring grammar and order. For example, “cat sat on mat” is treated the same as “on mat cat sat.” Though simple and order-blind, BoW proved powerful for tasks like spam detection and topic classification.

Educative byte: BoW gained momentum in the 1990s with vector space models and early search engines, paving the way for modern text retrieval techniques.

You have two short sentences: “I love cats” and “I hate dogs.” First, you gather all the unique words from both sentences into a vocabulary: namely ["I", "love", "cats", "hate", "dogs"]. Next, you count how many times each vocabulary word appears in each sentence. The sentence “I love cats” includes “I,” “love,” and “cats” once each, so it transforms into the vector [1, 1, 1, 0, 0] (corresponding to the order in your vocabulary). Meanwhile, “I hate dogs” contains “I,” “hate,” and “dogs” once apiece, giving us [1, 0, 0, 1, 1].

Bag of words example
Bag of words example

In Python, for instance, you can use CountVectorizer from scikit-learn to handle tokenization and counting automatically.

Python
from sklearn.feature_extraction.text import CountVectorizer
sentences = ["I love cats", "I hate dogs"]
vectorizer = CountVectorizer(token_pattern=r'(?u)\b\w+\b')  # Adjusted pattern to include single characters
bow_matrix = vectorizer.fit_transform(sentences)
print("Vocabulary:", vectorizer.get_feature_names_out())
print("Vectors:\n", bow_matrix.toarray())

Bag of Words is fast and straightforward, which made it popular in early search engines and text classification. It counts how often each word appears in a text, which works well for grouping documents by topic.

However, it cannot capture meaning in tasks like sentiment analysis or translation, where word order matters. Even with this weakness, BoW was a big leap because it turned raw language into structured features that computers could process effectively.

What is TF-IDF?

A bag of Words counts word occurrences but treats every word the same, so common terms like “the” or “is” can overshadow more meaningful ones. This makes it hard to capture what truly defines a document.

That’s where TF-IDF (Term Frequency–Inverse Document Frequency) steps in. TF-IDF addresses this by assigning higher weights to words that are frequent in a particular document but rare across all documents. It’s like saying, “Hey, I know ‘the’ appears a thousand times, but maybe that’s just because it’s a common word. Let’s not give it too much weight.” This way, words that are unique to specific documents stand out, while common words are downplayed.

Let’s break down the concept into two parts:

  • Term Frequency (TF) measures how often a word appears in a single document relative to the total number of words in that document. For instance, in a product review, the word "durable" might appear frequently if many customers praise the product’s longevity. Mathematically, it's expressed as:

  • Inverse Document Frequency (IDF) gauges how rare or special a word is across the entire corpusA collection of documents or texts. Common words like "the," "and," or "is" appear in nearly every document, rendering them less informative. In contrast, unique words like "durable" or ...