Word Embeddings

Continuous vector representations of words learned from large corpora that capture semantic and syntactic relationships. Word embeddings form the foundation for modern NLP systems by representing words in a dense vector space where similar words have similar representations.

Key Papers

• Distributed Representations of Words and Phrases and their Compositionality — Extends word embedding methods with phrase representations and techniques (negative sampling, subsampling) that improve both speed and accuracy; demonstrates compositional structure allowing meaningful vector addition

Related topics

• Text Representations (broader category of representation learning)
• Natural Language Processing (broader NLP methods)
• Phrase Embeddings (extension to multi-word units)