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Bayesian Analysis in Natural Language Processing

自然语言处理中的贝叶斯分析:第二版

丛 书 名:Synthesis lectures on human language technologies,

版本说明:Second edition.

作     者:Shay Cohen 

I S B N:(纸本) 9781681735283 

出 版 社:Morgan & Claypool Publishers 

出 版 年:2019年

学科分类:12[管理学] 07[理学] 08[工学] 071102[理学-系统分析与集成] 0711[理学-系统科学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081203[工学-计算机应用技术] 081104[工学-模式识别与智能系统] 0835[工学-软件工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081103[工学-系统工程] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

摘      要:Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze *** then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction *** this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed in-house in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.

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