From Linguistics to Technology

When I first heard developers speak of tokenisation and lemmatisation, the words felt oddly familiar. They were not foreign concepts to me. In corpus linguistics, I had spent years slicing language into parts, tracing roots, and following how meaning shifts with form. What the tech world presented as “jargon” was, for me, an old landscape seen with a new compass. What I never imagined was that my once tech-hesitant self would one day navigate these processes confidently at a console I had once treated like a dangerous animal.

Translating Skills Across Worlds

For two decades in academia, I taught and researched across the breadth of linguistics: semantics, morphology, pragmatics, discourse analysis, corpus methods, conceptual metaphor, and intercultural communication. I wrote about clichés and evaluation, explored sentiment in marketing and health communication, and analysed fear and resilience during the first months of COVID-19. Each of these projects gave me insight into how language frames experience, not as abstraction but as lived reality.

Language was never simply an object of study. It was a way of entering human experience — how we narrate pain, express desire, negotiate identity, or endure suffering. I learned that a single metaphor can open the door to empathy or close it completely. These are the same insights that make the difference between a tool that merely processes words and one that genuinely listens to the people behind them.

Building Tools with Purpose

Out of both research and lived experience came a tool I once wished had existed for me: Explain My Pain. It began as a metaphor tagger for research purposes, but grew into a web app designed to help patients describe their pain in ways that clinicians can understand. What started as academic exploration and personal necessity became a bridge across a communication gap that had long felt unbridgeable.

I never set out to “become a developer.” I set out to solve a problem. That path required me to learn Python, front-end development, and APIs — and, somewhere along the way, I realised I was already practising natural language processing. The same skills I had developed as a linguist — pattern recognition, meaning-mapping, contextual interpretation — were the very ones needed to teach a machine how to listen more like a human.

Where I Fit Now

NLP became more than a technical challenge. It became a new canvas for everything I knew about language. Tokenisation was no longer just splitting text — it was honouring each fragment of a person’s story. Lemmas were not mechanical dictionary forms but the essential shapes of meaning beneath the surface. Sentiment analysis was not simply numbers on a scale but traces of fear, hope, or determination flowing through a sentence.

This work demands code, but it also demands empathy. Algorithms can parse what is said, but linguistics helps them sense why it matters. Grammar, pragmatics, culture, idioms, and discourse markers are not peripheral — they are the heart of meaning.

Today I stand at a rare intersection: linguist, developer, writer, researcher, patient, and builder. I design figurative-language taxonomies, analyse crisis discourse, map how metaphors carry fear or resolve, and transform these insights into working software. In a world where technology plays a pivotal role, I believe linguistics can give machines a deeper kind of understanding. That intersection — where language meets NLP — feels like home.