Distributed Representations

Encodes words or phrases as vectors, using language’s distributional properties from large datasets. Central to advanced NLP, this method supports deep learning by encapsulating semantic and syntactic subtleties in vectors, enabling nuanced language understanding. Word embeddings exemplify this, mapping words into vector spaces where relationships are defined by vector proximity. Such representations are pivotal for processing and comprehending complex language patterns, enhancing the performance of NLP applications by providing depth and accuracy in machine interpretation of text.