Imagine, for a moment, the year is 1965. The air hums not with the quiet whir of a MacBook, but with the clatter of machinery and the mechanical breath of room-sized mainframe computers. In this world, “coding” doesn’t involve sleek text editors and instant compilation; it involves meticulously punching holes into stacks of stiff manila cards. It is here, in this era of technological infancy, that a handful of pioneers decided to teach a machine one of humanity’s oldest and most intricate skills: grammar.
Long before we could ask Siri for the weather or have Google Translate render a webpage from Japanese, the first computational linguists took on a monumental task. Their subject wasn’t the messy, exception-riddled syntax of English. It was the elegant, logical, and deeply structured world of Latin. Their first lesson: teaching a computer to conjugate a verb. To learn, in its own binary way, the rhythm of amo, amas, amat.
The choice of Latin was no accident. For a computer that understood nothing but absolute logic and binary instructions, a language like English was a nightmare. Consider the verb “to run.” I run, you run, he runs. Why the ‘s’? Now consider the past tense: I ran. Where did “runned” go? English is a patchwork of Germanic roots, French influences, and centuries of linguistic drift, leaving it full of exceptions, irregular verbs, and contextual nuance.
Latin, in contrast, is a linguist’s dream of order and predictability. It is a highly inflected language, meaning that words change their form to express grammatical function. While this creates a lot of endings to memorize for a human student, for a computer, it’s a goldmine of patterns. A Latin verb’s ending tells you everything you need to know:
For the pioneers of computational linguistics, this regularity was key. If they could codify these rules, they could prove that the core structures of language were, in fact, computable.
The first step wasn’t about computers at all; it was pure linguistic analysis. How do you conjugate the verb amāre (to love)? A human student learns through rote memorization and pattern recognition. A computer needed a precise, step-by-step procedure, or an algorithm.
The linguists broke the verb down into its essential components:
The fundamental rule became deceptively simple: Final Verb Form = Stem + Ending. The challenge was teaching the machine how to find the right stem and select the right ending from a pre-defined table of possibilities. A rule for finding the stem might look something like this (in plain English): “For a first-conjugation verb ending in -āre, remove the -āre to find the stem.”
With the linguistic rules defined, the computer scientists got to work. Their task was to translate these rules into a format the computer could process. This meant punch cards. Each card was a line of code or a piece of data. A stack of cards formed a program.
Imagine a simplified program flow for conjugating amāre in the first-person singular present:
This process, though clunky by today’s standards, was revolutionary. It demonstrated that morphology—the study of word formation—could be modeled as a system of symbolic manipulation. It was a formal, repeatable, and verifiable process.
Of course, no language, not even Latin, is perfectly regular. What about the verb “to be” (esse)? Its present tense conjugation is sum, es, est, sumus, estis, sunt. There’s no consistent stem-plus-ending pattern here. These irregular verbs were the bane of early systems.
The programmers had to build in exceptions. The algorithm would first have to check if the verb was on a special “list of irregulars.” If it was, the program would ignore the standard conjugation rules and simply look up the correct form in a hard-coded table. This rule-based approach with exception handling was a foundational concept. It showed both the power and the brittleness of the method: it worked beautifully until it hit a case the programmers hadn’t anticipated.
The linguists and programmers of the 1960s never built a fluent, Latin-speaking AI. That wasn’t the goal. Their achievement was proving that a machine could process and generate human language based on a formal model of its grammar. This was the birth of computational linguistics as a practical field.
The direct descendants of these simple algorithms are all around us. When your word processor puts a red squiggly line under “runned”, it’s using a more sophisticated version of that same logic—checking the word against a dictionary of stems, morphological rules, and a list of irregulars. When a search engine understands that “running” and “ran” are related to “run”, it’s performing morphological analysis pioneered in these early experiments.
Today, language technology has moved on to complex statistical models and neural networks. But the fundamental lesson remains. Before an AI can write a poem or translate a conversation, it must, on some level, understand structure. The pioneering work of teaching a room-sized computer to say “I love” was the first critical step on a journey that continues to reshape our world.
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