WORD2VEC Overview

WORD2VEC is a group of related models used to produce WORD-EMBEDDINGS. These models are SHALLOW-NEURAL-NETWORKS that are trained to reconstruct linguistic contexts of words. It was created by TOMAS-MIKOLOV and his team at GOOGLE. The technology revolutionized NATURAL-LANGUAGE-PROCESSING by allowing machines to understand the semantic meaning of words based on their distribution in a CORPUS.

The two primary architectures for WORD2VEC are the CBOW (Continuous Bag-of-Words) model and the SKIP-GRAM model. CBOW predicts the current word from a window of surrounding context words, while SKIP-GRAM uses the current word to predict the surrounding window of context words. Research papers such as Efficient Estimation of Word Representations in Vector Space highlight how these vectors can solve analogies using simple vector arithmetic, a phenomenon known as VECTOR-SPACE arithmetic.

By utilizing techniques like NEGATIVE-SAMPLING and HIERARCHICAL-SOFTMAX, WORD2VEC can be trained on massive datasets efficiently. This approach paved the way for more complex models in DEEP-LEARNING and ARTIFICIAL-INTELLIGENCE. More details can be found on the Google Archive page for Word2Vec.

• GLOVE
• BERT
• NEURAL-NETWORKS
• VECTOR-SPACE-MODELS