Before ‘cut and paste’ existed on a screen, there was the Friden Flexowriter, a revolutionary machine that used punched paper tape to edit text. Discover the story of this forgotten pioneer, which transformed writing from a permanent act into a process of data manipulation, paving the way for the digital world and computational linguistics.
Ever wonder how words like “rizz” become official while others fade away into obscurity? This post goes behind the scenes of lexicography, revealing the data-driven process that determines when a word is born and when it’s marked for death. Discover how you, as a speaker, hold the ultimate power in the life and death of language.
Before any AI can understand language, it must first shatter sentences into pieces through a process called tokenization. This crucial first step is far more complex than it seems, presenting unique linguistic puzzles across different languages, from English contractions to German compound nouns and Chinese text that has no spaces. This invisible labor, where computer science meets linguistics, is the foundational work that powers our entire digital world.
**
Before an AI can understand language, a human has to teach it. This work is done by linguistic annotators, the unsung heroes who manually tag text with grammatical and semantic information, creating the training data for models like GPT. This intricate process of “treebanking” and resolving linguistic ambiguity forms the very foundation of the AI revolution.
Ever wonder how we know exactly when to speak in a conversation? The field of Conversation Analysis reveals that our seemingly effortless chats are governed by a complex set of unwritten rules. This post deconstructs the hidden linguistic dance of turn-taking, from the subtle power of a pause to the intricate signals we use to yield the floor.
Ever wonder how new words like ‘rizz’ make it into the dictionary or why grammar rules seem to change over time? The answer lies in corpus linguistics, a fascinating field that uses massive, billion-word databases to analyze language as it’s actually used. This data-driven approach not only chronicles our language’s past but can even help predict its future.
