賈拉·薩拉基著的《Python自然語言處理(影印版)(英文版)》首先闡述了自然語言處理（Natural Language Processing，NLP）的基礎，以及為什麼Python是構建基於NLP的專家系統的最佳選擇之一，其中包括社區支持和可用框架等優勢。它還能夠使你更好地理解可用的免費語料庫以及不同類型的數據集。隨後，你會學到如何為NLP應用選擇數據集，找到正確的NLP技術來處理數據集中的句子並理解其結構。另外還將學習如何標記句子的不同部分並查看其分析方法。在閱讀本書的過程中，你將探索文本的語義和句法分析。了解如何解決處理人類語言時出現的各種歧義，碰到在執行文本分析時出現各種情況。你會學到設置NLP環境的基礎知識，初始化設置，然後快速理解句子和語言。你將領會到利用機器學習和深度學習從文本數據中提取信息的威力。在本書的結尾，你會對NLP有一個清晰的理解並在現實中實現多個NLP示例。
作者介紹
(印)賈拉·薩拉基
目錄
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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