基本信息
源码名称：Mastering Natural Language Processing with Python
源码大小：0.60M
文件格式：.epub
开发语言：Python
更新时间：2021-06-30
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源码介绍
Chapter 1, Working with Strings, explains how to perform preprocessing tasks on text, such as tokenization and normalization, and also explains various string matching measures.
Chapter 2, Statistical Language Modeling, covers how to calculate word frequencies and perform various language modeling techniques.
Chapter 3, Morphology – Getting Our Feet Wet, talks about how to develop a stemmer, morphological analyzer, and morphological generator.
Chapter 4, Parts-of-Speech Tagging – Identifying Words, explains Parts-of-Speech tagging and statistical modeling involving the n-gram approach .
Chapter 5, Parsing – Analyzing Training Data, provides information on the concepts of Tree bank construction, CFG construction, the CYK algorithm, the Chart Parsing algorithm, and transliteration.
Chapter 6, Semantic Analysis – Meaning Matters, talks about the concept and application of Shallow Semantic Analysis (that is, NER) and WSD using Wordnet.
Chapter 7, Sentiment Analysis – I Am Happy, provides information to help you understand and apply the concepts of sentiment analysis.
Chapter 8, Information Retrieval – Accessing Information, will help you understand and apply the concepts of information retrieval and text summarization.
Chapter 1, Working with Strings, explains how to perform preprocessing tasks on text, such as tokenization and normalization, and also explains various string matching measures.
Chapter 2, Statistical Language Modeling, covers how to calculate word frequencies and perform various language modeling techniques.
Chapter 3, Morphology – Getting Our Feet Wet, talks about how to develop a stemmer, morphological analyzer, and morphological generator.
Chapter 4, Parts-of-Speech Tagging – Identifying Words, explains Parts-of-Speech tagging and statistical modeling involving the n-gram approach .
Chapter 5, Parsing – Analyzing Training Data, provides information on the concepts of Tree bank construction, CFG construction, the CYK algorithm, the Chart Parsing algorithm, and transliteration.
Chapter 6, Semantic Analysis – Meaning Matters, talks about the concept and application of Shallow Semantic Analysis (that is, NER) and WSD using Wordnet.
Chapter 7, Sentiment Analysis – I Am Happy, provides information to help you understand and apply the concepts of sentiment analysis.
Chapter 8, Information Retrieval – Accessing Information, will help you understand and apply the concepts of information retrieval and text summarization.