Package smile.nlp.embedding
package smile.nlp.embedding
Word embedding. Word embedding is the collective name for a set
of language modeling and feature learning techniques in natural
language processing where words or phrases from the vocabulary
are mapped to vectors of real numbers. Conceptually it involves
a mathematical embedding from a space with many dimensions per
word to a continuous vector space with a much lower dimension.
Methods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in terms of the context in which words appear.
Word and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis.