High-Dimensional Vector Embeddings in NLP
High-dimensional vector embeddings in natural language processing constitute a mathematical framework where semantic information is represented within continuous dense spaces via numerical vectors. This mechanism relies on the principle that lexical and syntactic relationships map to geometric structures, allowing operations such as addition or subtraction of vectors to reflect analogical reasoning between concepts. Formally defined by dimensionality (d) and embedding matrices derived from distributional hypotheses, this subfield operates within computational linguistics and machine learning theory rather than discrete symbolic logic.
Word Embeddings in NLP: Vector Representations and Transformer Contextualization
Word embeddings represent lexical items as continuous numeric vectors within a high-dimensional space to encode semantic relationships and contextual information that standard machine learning algori…