《Natural Language Processing with PyTorch》 Chapter 2： A Quick Tour of Traditional NLP 笔记

这本书

本章的笔记里面涉及的理论内容在我记录的西湖大学张岳老师的introduction那个笔记里面基本都有提及，不过这篇主要是利用spaCy和NLTK做了一些实践

文章目录

《Natural Language Processing with PyTorch》 Chapter 2： A Quick Tour of Traditional NLP 笔记
Corpora（语料库）, Tokens, and Types
- Tokenization
- - 用NLTK、spaCy进行tokenization
  - - spaCy
    - NLTK
Unigrams, Bigrams, Trigrams, …, N-grams
Lemmas and Stems 基本词形和词根
- lemma
- stems
Categorizing Sentences and Documents
Categorizing Words: POS Tagging 词性标注
Categorizing Spans: Chunking and Named Entity Recognition
- Chunking
- NER
Structure of Sentences
Word Senses and Semantics 词义和语义
Summary
参考资料

The goal of the chapter
Review some traditional NLP concepts and methods，书的后面部分主要讲深度学习
NLP & CL

We only focus on NLP in this book, but we may incorporate ideas from CL as necessary.
- Natural language processing (NLP, introduced in the previous chapter) and computa‐ tional linguistics (CL) are two areas of computational study of human language.
  - NLP : NLP aims to develop methods for solving practical problems involving language, such as information extraction, automatic speech recognition, machine translation, sentiment analysis, question answering, and summarization.
  - CL ： CL employs computational methods to understand properties of human language.
这本书的文笔感觉不错哈哈哈，我练练转述顺便学习一下原来的表达
- In literature（文献中）, it is common to see a crossover of methods and researchers, from CL to NLP and vice versa（反之亦然）.(It is common for CL and NLP to borrow methods and ideas from each other. )
  
  Lessons from CL about language can be used to inform priors in NLP（指导NLP中的先验知识）, and statistical and machine learning methods from NLP can be applied to answer questions CL seeks to answer.
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《Natural Language Processing with PyTorch》 Chapter 2： A Quick Tour of Traditional NLP 笔记

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