Vectorizing Language
Explore how transforming sparse text into dense word embeddings revolutionized NLP and powered modern GenAI.
Traditional NLP methods such as rule-based systems, Bag of Words, TF-IDF, and n-grams represent text by counting word occurrences. This works for basic tasks such as classification or prediction, but it treats words as isolated tokens with no sense of meaning or connection.
For example, “cat” and “feline” are seen as completely unrelated, even though they describe the same animal. Likewise, words like “great,” “terrific,” and “awesome” are not recognized as expressing similar sentiments. Frequency-based methods can only note co-occurrences, not capture true relationships.
Traditional methods treat words as independent units, like giving each guest at an event a unique badge number. The system can track who is present but knows nothing about relationships between guests. In the same way, frequency-based models count words but miss their connections. They also create huge, sparse representations that require lots of data yet struggle to generalize.
What are word embeddings?
Counting words was not enough, so researchers developed word embeddings to capture meaning. Instead of treating each word as an isolated unit, embeddings represent words as vectors in a continuous space where similar words appear closer together. For example, “king” and “queen” will be close in the vector space, and the relationship between “man” and “woman” parallels that of “king” and “queen.”
These vectors are dense, meaning they use far fewer dimensions than Bag of Words or TF-IDF, yet carry much richer information. Each dimension reflects some linguistic feature, such as topic, sentiment, or grammatical role. This compact, meaningful representation allows models to generalize better, learn patterns faster, and apply knowledge across different contexts.
Word embeddings became the bridge between simple frequency-based methods and modern deep learning, powering breakthroughs like Word2Vec and GloVe. ...