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.

What Exactly is Diachronic Analysis?

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.

The NLP Magic: How Computers Learn History

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.

The Data: A Digital Library of Alexandria

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:

• Google Books Corpus: Millions of scanned books dating back to the 16th century. The Google Ngram Viewer is a simple, public-facing tool that uses this data to show word frequency over time.
• Historical Newspaper Archives: Digital collections from newspapers like the New York Times or the Times of London provide a dated snapshot of common language use.
• Project Gutenberg: A library of over 70,000 free eBooks, mostly older works whose copyrights have expired.
• Social Media and Web Archives: For more recent changes, researchers use data from platforms like Twitter (X) and Reddit, or the Common Crawl web archive.

The Method: Word Embeddings and “Meaning Space”

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.

• Words like “king”, “queen”, and “prince” will be clustered together because they often appear near words like “royal”, “throne”, and “kingdom.”
• Words like “bread”, “butter”, and “flour” will occupy another neighborhood on the map.

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.

Words on the Move: Fascinating Case Studies

This computational approach has confirmed and uncovered fascinating semantic journeys.

‘Gay’

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.

‘Awful’ and ‘Terrible’

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.”

‘Nice’

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’.

‘Literally’

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’.

Why We Dig Through Digital Dust: The Importance of Diachronic NLP

This isn’t just an academic exercise. Understanding semantic change has profound, practical applications:

• Historical & Cultural Insights: Tracing word meanings allows us to map societal changes in values, biases, and focus. The evolution of words related to gender, technology, or politics is a mirror to our own history.
• Improving AI: To properly understand historical documents, legal texts, or classic literature, an AI must know what words meant at the time they were written.
• Lexicography: Diachronic analysis provides dictionary-makers with quantitative evidence for when and how to update definitions.
• Literary Analysis: It helps us read Shakespeare or Jane Austen with a clearer understanding of how their original audiences would have received their language.

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.