Introduction to Natural Language Processing 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Introduction to Natural Language Processing

簡體網頁||繁體網頁
Jacob Eisenstein 作者
The MIT Press
譯者
2019-10-1 出版日期
536 頁數
USD 75.00 價格
Paperback
叢書系列
9780262042840 圖書編碼

Introduction to Natural Language Processing 在線電子書 圖書標籤: NLP  計算機科學  計算機  Linguistica  AI  方法  數據科學   


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Introduction to Natural Language Processing 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Introduction to Natural Language Processing 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Introduction to Natural Language Processing 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



Introduction to Natural Language Processing 在線電子書 用戶評價

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thorough and detailed! One of the best NLP books! Quite worth reading!

評分

thorough and detailed! One of the best NLP books! Quite worth reading!

評分

thorough and detailed! One of the best NLP books! Quite worth reading!

評分

thorough and detailed! One of the best NLP books! Quite worth reading!

評分

thorough and detailed! One of the best NLP books! Quite worth reading!

Introduction to Natural Language Processing 在線電子書 著者簡介

I work on computational linguistics, focusing on non-standard language, discourse, computational social science, and machine learning. In July 2019, I joined Google AI as a research scientist. From 2012 to 2019, I was on the faculty at Georgia Tech, where I led the Computational Linguistics lab.


Introduction to Natural Language Processing 在線電子書 著者簡介


Introduction to Natural Language Processing 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Introduction to Natural Language Processing 在線電子書 圖書描述

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.

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