If you describe your meal as “awful”, you’re not paying it a compliment. But if you were an English speaker in the 14th century, you would be. Back then, “awful” meant exactly what its parts suggest: “full of awe.” It was a word reserved for things that were inspiring, majestic, or even terrifyingly divine.
So, what happened? How did a word for sublimity become a synonym for “very bad”?
This journey of a word through time is at the heart of diachronic analysis. For centuries, this was the painstaking work of linguists and historians, poring over fragile manuscripts and dusty books. Today, thanks to the power of Natural Language Processing (NLP), computers can trace these changes on a massive scale, giving us an unprecedented view into the living, breathing evolution of language.
Let’s break down the term. “Diachronic” comes from the Greek words dia- (“across”) and chronos (“time”). So, diachronic analysis is simply the study of language across time. It focuses on evolution, tracking how sounds, grammar, and, most famously, word meanings (semantics) shift over decades or centuries.
This is the opposite of synchronic analysis (syn- meaning “with” or “at the same time”), which studies a language as a complete system at a single point in time. A synchronic study might analyze the slang used by teenagers in 2024, while a diachronic study would track how the meaning of the word “teenager” itself has changed since it first appeared.
For most of history, diachronic linguistics was a manual, detective-like process. But the digital revolution changed everything.
How can a machine, which understands code and logic, possibly grasp something as fluid and culturally embedded as semantic change? The answer lies in two key components: massive datasets and clever algorithms.
Computers need something to read, and luckily, we’ve been digitizing our textual history for decades. Diachronic NLP models are trained on colossal text archives, such as:
This is where the real magic happens. NLP doesn’t “understand” a word like a human does. Instead, it learns meaning from context. The core technique used is called word embedding.
Imagine a giant, multi-dimensional map—a “meaning space.” The NLP model gives every single word a set of coordinates on this map. How does it decide where to place a word? Based on the words that typically appear around it.
To perform diachronic analysis, researchers train separate word embedding models on texts from different time periods. For example, they might create one model for texts from 1850-1900 and another for texts from 1980-2020.
Then, they track the coordinates of a specific word. If a word’s coordinates have moved significantly from one model to the next, its meaning has changed. The algorithm has detected semantic shift by noticing that the word’s neighbors—its context—are different now.
This computational approach has confirmed and uncovered fascinating semantic journeys.
Perhaps the most cited example. By analyzing texts from the 19th and early 20th centuries, a diachronic model would place ‘gay’ near words like ‘happy’, ‘jolly’, ‘bright’, and ‘carefree.’ By the late 20th century, its coordinates would have shifted dramatically, clustering it with words like ‘homosexual’, ‘lesbian’, ‘pride’, and ‘queer.’ The model quantifies this cultural and linguistic evolution from a general emotion to a specific identity.
As we saw, ‘awful’ used to mean “awe-inspiring.” The same is true for ‘terrible’, which originally meant “to inspire terror or awe”, often in a religious context. Both words underwent a process called pejoration, where their meaning became more negative over time. Today, they are weak synonyms for “unpleasant.”
This word experienced the opposite journey: amelioration. In the 14th century, ‘nice’ meant ‘foolish’ or ‘ignorant’ (from the Latin nescius, meaning “unaware”). Over centuries, its meaning softened to ‘coy’, then ‘precise’ or ‘fussy’ (“a nice distinction”), and finally arrived at its modern meaning of ‘pleasant’ and ‘kind’.
A change happening in our lifetime. For generations, ‘literally’ meant ‘in a strict, non-figurative sense.’ Today, it is frequently used as a general intensifier to add emphasis, often in a figurative context (“I was literally dying of laughter”). A diachronic analysis of web data from 2000 vs. 2020 would show its coordinates moving closer to words like ‘really’ and ‘very’.
This isn’t just an academic exercise. Understanding semantic change has profound, practical applications:
Language is not a stone monument, fixed and eternal. It is a flowing river, constantly carving new paths and carrying the sediment of our collective experience. With diachronic analysis, NLP has given us a powerful satellite view of that river, allowing us to see not just where it is now, but the entire, winding path it took to get here.
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