The Emergence of NLP

Imagine a future where machines achieve singularity—that pivotal moment when artificial intelligence surpasses human intellect, reshaping every facet of our lives. While it still sounds like science fiction, the astonishing capabilities of generative AI are nudging us closer to this reality every day. From crafting poetic lines to devising groundbreaking solutions in science and engineering, generative AI stands poised not just as a tool but a potential creative partner.

Yet, for all the revolutionary outcomes these models can produce, their success hinges on a foundational discipline: natural language processing (NLP). NLP enables machines to recognize words, parse sentences, and interpret context. Without these fundamental capabilities—transforming raw text into representations that computers can understand—there would be no ChatGPT, no DALL•E, and advanced recommendation engines. Think about it—before any AI can generate thoughtful text or creative art, it must first learn to read our language. NLP is the quiet workhorse powering every breakthrough in generative technology.

This lesson explores how NLP has evolved from simple rule-based systems to sophisticated approaches like a bag of words, TF-IDF, n-gram models, and word embeddings. Every leap in NLP—from rules to statistics to deep learning—was driven by a single question: “How do we get machines to truly ‘understand’ language?” That same question is fueling the development of advanced generative AI today. By understanding these breakthroughs, we see how they set the stage for today’s large language models—models edging us ever closer to what some consider the cusp of AGI (Artificial General Intelligence).

How did computers first interpret the text?

Suppose you’re trying to teach a toddler grammar by handing them a thick manual that says, “Whenever you see ‘he is,’ swap it to ‘he’s’; if you find a verb after ‘to,’ don’t add ‘-s.’” That’s essentially how rule-based NLP worked: linguists and developers painstakingly wrote if-then instructions for every language quirk. Computers would scan text, match it against these handcrafted patterns, and produce outputs—sometimes correct, often hilariously wrong if the text didn’t match their narrow rules. While rule-based methods could handle small, domain-specific tasks (like checking subject-verb agreement), they were famously brittle. Encounter a new phrase or slightly off-the-beaten-path syntax, and the system would crumble. Notice how this “if-then” approach is so rigid—imagine trying to write a rule for every possible way someone might say hello. This limitation drove the need for systems that could learn from data instead of relying solely on fixed rules.

In the early days of NLP, this was our best: a rigid, recipe-like approach. But as language changed and datasets grew, the idea of manually encoding every possibility became impractical. Rule-based systems had computers looking at language through a straw—narrow and easily broken. The transition to data-driven methods let machines ‘see’ the bigger picture of word usage and frequency, which was crucial for the rise of modern NLP. This frustration paved the way for a more flexible, data-driven solution: instead of telling machines exactly how to interpret text, we let them learn from word counts and usage patterns. The next steps harnessed the power of statistical and machine learning methods to learn from data rather than rely on an ever-expanding set of rules.

What is a bag of words?

One of the earliest statistical methods to gain wide traction was bag of words (BoW), which departed from rule-based instructions and instead relied on counting word occurrences. Bag of Words became popular for text classification and information retrieval in the 1960s, and it remained a cornerstone for quite some time.

Imagine discussing your favorite novel with a friend—but instead of explaining the plot, you dump all the book’s words into a bag and count how often each appears. That’s a bag of words (BoW): it treats a document (sentence, paragraph, or entire book) as an unordered collection of words, ignoring grammar or sequence. However, word order doesn’t matter here—“cat sat on mat” is treated the same as “on mat cat sat.” We only care about how many times each word shows up. Even though it seems crude—losing all word order—BoW is surprisingly powerful for tasks like spam detection or topic classification, where certain words’ presence (or absence) speaks volumes about the text’s meaning.

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].

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

When we run the code, we can notice that bag of words doesn’t capture the order of words—if someone wrote “dogs hate I,” you’d end up with the same counts. It’s as if we tallied how often each word from our vocabulary appears in each sentence, ignoring word order or context. Bag of words was instrumental in the development of early search engines, allowing them to index and retrieve documents based on word frequency, laying the groundwork for more advanced information retrieval systems.

Yes, this was fast and straightforward, and often used in early text classification tasks. However, it couldn't distinguish “I hate dogs” from “dogs hate I,” because both produce the same counts. This loss of word order and contextual clues means that the underlying meaning can be misunderstood, which is critical in applications like sentiment analysis and machine translation. For some tasks, “cat sat on mat” vs. “on mat cat sat” makes no difference. But for sentiment or nuance, that can matter a lot. The simplicity of BoW was a big leap, yet it left plenty of room to grow.

We can feed those vectors into any machine learning algorithm that handles numeric data—like Naive Bayes, logistic regression, or SVM. For example, if you’re building a spam detector, your model might learn that emails containing the word “lottery” frequently are spam. Essentially, BoW turns raw language into structured features, enabling conventional ML techniques.

What is TF-IDF?

While bag of words provides a straightforward method to represent text by counting word occurrences, it has a significant limitation: it treats all ...