This summer, I got my first taste of college life—or at least, the online version—through Ling 234 at UW. If you’re imagining grand lecture halls and bustling campus energy, this was not that. Instead, it was me, my laptop, and a series of online modules. But don’t let the format fool you—this class packed a lot of depth.
I took Ling 234 to get a deeper understanding of the linguistic concepts that underpin computational linguistics. As someone interested in the intersection of language and technology, I wanted to explore the “deep end” of linguistics: how societies perceive language, how languages vary and change, and how they influence identity. Understanding topics like language ideologies, multilingualism, and even the sociolinguistics of dialects helps ground the technical aspects of computational linguistics in real-world human language complexities.
One of the most valuable connections I found was how language variation and sociolinguistic factors affect language processing. For example, concepts like dialect variation, multilingualism, and even gendered language use are critical when developing systems that work across diverse language contexts. Computational linguistics relies on handling these nuances, whether in sentiment analysis, machine translation, or speech recognition. The insights I gained from this course are stepping stones to building more inclusive and accurate models in AI.
If you’re curious, I’ve attached my notes for the class. They’re comprehensive and detail everything from the mechanics of language contact to the challenges of language revitalization. While these notes may not be everyone’s cup of tea, they represent a foundational step in my journey toward understanding language through both linguistic and computational lenses.
Ling 234 may not have had the traditional “college experience” feel, but it exceeded my expectations in laying the groundwork for integrating linguistics into computational approaches. It wasn’t just a class—it was a valuable perspective shift.