About this document
Understanding Vector Space Models by dibu23.afa is a document available to read on EtoBox.
The document discusses vector space models, which represent words and documents as vectors to capture relative meanings and similarities. It covers applications in information extraction, machine translation, and chatbots, as well as methods for calculating similarity, including Euclidean distance and cosine similarity. Additionally, it addresses the use of Principal Component Analysis (PCA) for visualizing word vectors and reducing dimensionality while retaining information.
- Author
- dibu23.afa
- Language
- EN