Word Embeddings: Turning Words into Vectors
Word embeddings turn words into numerical vectors, like coordinates on a map of meaning. Words with similar meanings, like "king" and "queen," are placed close together in this vector space. This is fundamental for text analysis in machine learning, allowing models to grasp semantic relationships instead of just matching text. The footgun is assuming the vector's individual numbers are human-interpretable; they are abstract features learned from data.
Word embeddings transform words from text into dense numerical vectors, essentially giving each word a coordinate in a high-dimensional "meaning space." In this space, proximity equals semantic similarity, so "cat" is closer to "kitten" than to "car." This is how NLP models perform tasks like sentiment analysis or machine translation by understanding word relationships mathematically. The core footgun is treating the vector's dimensions as directly meaningful, like "dimension #10 means 'royalty'." These dimensions are abstract features learned by the model, not human-defined attributes.
Read the original → Wikipedia: Word embedding
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