Embaddings

Word Embeddings:Word embeddings are numerical representations of words in a continuous vector space. They are learned from large text corpora using techniques such as Word2Vec, GloVe (Global Vectors for Word Representation), or FastText. Word embeddings encode semantic relationships between words based on their co-occurrence patterns in the training data. For example, words with similar meanings or usage contexts tend to have similar embeddings, and relationships between words can be captured through vector arithmetic (e.g., "king" - "man" + "woman" ≈ "queen").
Phrase and Document Embeddings:In addition to word embeddings, embeddings can also be learned for larger textual units such as phrases or entire documents. Phrase embeddings capture the semantic meaning of multi-word expressions, while document embeddings represent the overall content and context of a document. Techniques such as Doc2Vec and paragraph embeddings from models like BERT (Bidirectional Encoder Representations from Transformers) are commonly used for learning phrase and document embeddings. Feature Space: Embeddings map words or documents from a high-dimensional space (the vocabulary or document space) to a lower-dimensional continuous vector space (the embedding space). This transformation preserves semantic relationships and allows algorithms to operate more efficiently on textual data.