Decoding Word Relationships: Synonymy, One-Hot Encoding, and TF-IDF in Generative AI

In the latest lecture of a generative AI course, the instructor delves into foundational techniques for representing and analyzing word meaning, covering synonymy, similarity, one-hot vectors, term frequency (TF), and TF-IDF. The session begins with an exploration of word synonymy, similarity, and relatedness, explaining how words can be interchangeable in certain contexts yet differ in nuance. The lecture then shifts to one-hot vectors, a basic method for encoding words as binary vectors, followed by term frequency and the bag-of-words model, which counts word occurrences in documents. Finally, TF-IDF (term frequency-inverse document frequency) is introduced as a more sophisticated weighting scheme that balances local and global word importance, highlighting its role in information retrieval and text mining.