CBOW
CBOW, short for Continuous Bag of Words, is a neural network model used in the Word2Vec family to learn dense vector representations of words from large text corpora. It is trained to predict a target word from its surrounding context words within a fixed window.
In CBOW, the input consists of several context words. Each context word is mapped to a continuous
Training CBOW efficiently over large vocabularies often involves approximations such as negative sampling or hierarchical softmax,
CBOW is contrasted with the Skip-gram model, which learns to predict context words from a target word.
Limitations include reliance on fixed context windows, ignoring word order and long-range dependencies, and reduced effectiveness