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Titlebook: Bayesian Analysis in Natural Language Processing, Second Edition; Shay Cohen Book 2019Latest edition Springer Nature Switzerland AG 2019

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發(fā)表于 2025-3-21 19:59:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bayesian Analysis in Natural Language Processing, Second Edition
影響因子2023Shay Cohen
視頻videohttp://file.papertrans.cn/182/181823/181823.mp4
學(xué)科分類(lèi)Synthesis Lectures on Human Language Technologies
圖書(shū)封面Titlebook: Bayesian Analysis in Natural Language Processing, Second Edition; Shay Cohen Book 2019Latest edition Springer Nature Switzerland AG 2019
影響因子.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 language. Since 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 examples...In 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 n
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Uday Athavankar,Arnab Mukherjeeare too many components in the model: much of the slack in the number of clusters will be used to represent the noise in the data, and create overly fine-grained clusters that should otherwise be merged together.
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1947-4040 , 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 n978-3-031-01042-2978-3-031-02170-1Series ISSN 1947-4040 Series E-ISSN 1947-4059
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Bayesian Analysis in Natural Language Processing, Second Edition978-3-031-02170-1Series ISSN 1947-4040 Series E-ISSN 1947-4059
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https://doi.org/10.1007/978-1-4613-2173-6 a computer. As such, it borrows ideas from Artificial Intelligence, Linguistics, Machine Learning, Formal Language Theory and Statistics. In NLP, natural language is usually represented as written text (as opposed to speech signals, which are more common in the area of Speech Processing).
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發(fā)表于 2025-3-23 01:06:02 | 只看該作者
Leonard Evans,Richard C. SchwingThis chapter is mainly intended to be used as a refresher on basic concepts in Probability and Statistics required for the full comprehension of this book. Occasionally, it also provides notation that will be used in subsequent chapters in this book.
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