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/*
 * Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
 *
 * Smile is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Smile is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Smile.  If not, see .
 */

/**
 * 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. * * @author Haifeng Li */ package smile.nlp.embedding;





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