Foundations of Statistical Natural Language Processing
Title | Foundations of Statistical Natural Language Processing PDF eBook |
Author | Christopher Manning |
Publisher | MIT Press |
Total Pages | 719 |
Release | 1999-05-28 |
Genre | Language Arts & Disciplines |
ISBN | 0262303795 |
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.
Foundations of Statistical Natural Language Processing
Title | Foundations of Statistical Natural Language Processing PDF eBook |
Author | Christopher Manning |
Publisher | MIT Press |
Total Pages | 722 |
Release | 1999-05-28 |
Genre | Language Arts & Disciplines |
ISBN | 9780262133609 |
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.
Introduction to Natural Language Processing
Title | Introduction to Natural Language Processing PDF eBook |
Author | Jacob Eisenstein |
Publisher | MIT Press |
Total Pages | 535 |
Release | 2019-10-01 |
Genre | Computers |
ISBN | 0262042843 |
A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.
Speech & Language Processing
Title | Speech & Language Processing PDF eBook |
Author | Dan Jurafsky |
Publisher | Pearson Education India |
Total Pages | 912 |
Release | 2000-09 |
Genre | |
ISBN | 9788131716724 |
Mathematical Foundations of Speech and Language Processing
Title | Mathematical Foundations of Speech and Language Processing PDF eBook |
Author | Mark Johnson |
Publisher | Springer Science & Business Media |
Total Pages | 292 |
Release | 2012-12-06 |
Genre | Technology & Engineering |
ISBN | 1441990178 |
Speech and language technologies continue to grow in importance as they are used to create natural and efficient interfaces between people and machines, and to automatically transcribe, extract, analyze, and route information from high-volume streams of spoken and written information. The workshops on Mathematical Foundations of Speech Processing and Natural Language Modeling were held in the Fall of 2000 at the University of Minnesota's NSF-sponsored Institute for Mathematics and Its Applications, as part of a "Mathematics in Multimedia" year-long program. Each workshop brought together researchers in the respective technologies on the one hand, and mathematicians and statisticians on the other hand, for an intensive week of cross-fertilization. There is a long history of benefit from introducing mathematical techniques and ideas to speech and language technologies. Examples include the source-channel paradigm, hidden Markov models, decision trees, exponential models and formal languages theory. It is likely that new mathematical techniques, or novel applications of existing techniques, will once again prove pivotal for moving the field forward. This volume consists of original contributions presented by participants during the two workshops. Topics include language modeling, prosody, acoustic-phonetic modeling, and statistical methodology.
Bayesian Analysis in Natural Language Processing
Title | Bayesian Analysis in Natural Language Processing PDF eBook |
Author | Shay Cohen |
Publisher | Springer Nature |
Total Pages | 266 |
Release | 2022-11-10 |
Genre | Computers |
ISBN | 3031021614 |
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.
Statistical Methods for Speech Recognition
Title | Statistical Methods for Speech Recognition PDF eBook |
Author | Frederick Jelinek |
Publisher | MIT Press |
Total Pages | 307 |
Release | 2022-11-01 |
Genre | Language Arts & Disciplines |
ISBN | 0262546604 |
This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques. Bradford Books imprint