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smile-nlp

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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;