Téléchargez le livre : Bayesian Analysis in Natural Language Processing

Bayesian Analysis in Natural Language Processing



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Éditeur :

Springer


Collection :

Synthesis Lectures on Human Language Technologies

Paru le : 2022-11-10



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Description

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 for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples.
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. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling and their use with Bayesian analysis.
Pages
274 pages
Collection
Synthesis Lectures on Human Language Technologies
Parution
2022-11-10
Marque
Springer
EAN papier
9783031001758
EAN PDF
9783031021619
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
27
Taille du fichier
148480 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
108,44 €

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