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书名 | Python自然语言处理(影印版)(英文版) |
分类 | |
作者 | (印)贾拉·萨拉基 |
出版社 | 东南大学出版社 |
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简介 | 内容推荐 贾拉·萨拉基著的《Python自然语言处理(影印版)(英文版)》首先阐述了自然语言处理(Natural Language Processing,NLP)的基础,以及为什么Python是构建基于NLP的专家系统的最佳选择之一,其中包括社区支持和可用框架等优势。它还能够使你更好地理解可用的免费语料库以及不同类型的数据集。随后,你会学到如何为NLP应用选择数据集,找到正确的NLP技术来处理数据集中的句子并理解其结构。另外还将学习如何标记句子的不同部分并查看其分析方法。在阅读本书的过程中,你将探索文本的语义和句法分析。了解如何解决处理人类语言时出现的各种歧义,碰到在执行文本分析时出现各种情况。你会学到设置NLP环境的基础知识,初始化设置,然后快速理解句子和语言。你将领会到利用机器学习和深度学习从文本数据中提取信息的威力。在本书的结尾,你会对NLP有一个清晰的理解并在现实中实现多个NLP示例。 目录 Preface Chapter 1:Introduction Understanding natural language processing Understanding basic applications Understanding advanced applications Advantages of togetherness—N LP and Python Environment setup for NLTK Tips for readers Summary Chapter 2:Practical Understanding of a Corpus and Datase What is a corpus? Why do we need a corpus? UnderStanding corpus analysis Exercise Understanding types of data attributes Categorical or qualitative data attributes Numeric or quantitative data attributes Exploring different file formats for corpora Resources for accessing free corpora Preparing a dataset for NLP applications Selecting data Preprocessing the dataset Formatting Cleaning Sampling Transforming data Web scraping Summary Chapter 3:Understanding the Structure of a Sentences Understanding components of NLP Natural language understanding Natural language generation Differences between NLU and NLG Branches nf NLP Defining context-free grammar Exercise Morphological analysis What is morphology? What are morphemes? What is a stem? What is morphological analysis? What iS a word? Classification of morphemes Free morphemes Bound morphemes Derivational morphemes Inflectional morphemes What is the difference between a stem and a root? Exercise Lexical analysis Whal is a token? What are part of speech tags? Process of deriving tokens Difference between stemming and lemmatization Applications Syntactic analysis What is syntactic analysis? Semantic analysis What is semantic analysis? Lexical semantics Hyponymy and hyponyms Homonymy Polysemy What is the difference between polysemy and homonymy? Application of semantic analysis Handling ambiguity Lexical ambiguity Syntactic ambiguity Approach to handle syntactic ambiguity Semantic ambiguity Pragmatic ambiguity Discourse integration Applications Pragmatic analysis Summary Chapter 4: PreproceSSing Chapter 5: Feature Engineering and NLP Alclorithms Chapter 6:Advanced Feature Engineering and NLP Algorithms Chapter 7: Rule-Based System for NLP Chapter 8: Machine Learning for NLP Problems Chapter 9: Deep Learnincl for NLU and NLG Problems Chapter 10: Advanced Tools Chapter 11 : How to Improve Your NLP Skills Chapter 12: Installation Guide Index |
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