Python自然语言处理基础教程

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"Natural Language Processing with Python 是一本基础入门书籍,由 Steven Bird、Ewan Klein 和 Edward Loper 合著,旨在介绍使用 Python 进行自然语言处理(NLP)的相关知识。本书由 O'Reilly Media 出版,并提供在线版本。" 自然语言处理(NLP)是计算机科学领域的一个重要分支,它涉及计算机与人类(自然)语言之间的交互。Python 是 NLP 的首选编程语言,因为它具有丰富的库和简洁的语法,使得数据处理变得高效且易于理解。 本书"Natural Language Processing with Python"为初学者提供了深入浅出的教程,涵盖了 NLP 的基础知识以及如何利用 Python 实现这些概念。作者 Steven Bird、Ewan Klein 和 Edward Loper 都是 NLP 领域的专家,他们将他们的经验和知识转化为这本书,使读者能够快速上手。 书中可能涵盖的内容包括: 1. **文本预处理**:这是 NLP 的第一步,包括分词(tokenization)、去除停用词(stop words removal)、标点符号处理等,以准备原始文本数据供进一步分析。 2. **词性标注(Part-of-Speech Tagging)**:识别单词在句子中的角色,如名词、动词、形容词等,这对于理解和解析句子结构至关重要。 3. **命名实体识别(Named Entity Recognition, NER)**:找出文本中的专有名词,如人名、地名、组织名等,这对于信息提取和知识图谱构建很有帮助。 4. **句法分析(Syntactic Parsing)**:分析句子的结构,确定词汇之间的依赖关系,以理解句子的意义。 5. **情感分析(Sentiment Analysis)**:评估文本的情感倾向,如正面、负面或中立,常用于社交媒体分析和市场研究。 6. **机器翻译(Machine Translation)**:利用统计模型实现不同语言间的自动翻译。 7. **文本分类与信息检索**:通过训练模型将文本分类到不同的类别中,或根据查询从大量文档中检索相关信息。 8. **主题建模(Topic Modeling)**:发现文本集合中的隐藏主题,如 Latent Dirichlet Allocation (LDA)。 9. **深度学习在NLP中的应用**:如循环神经网络(RNN)、长短时记忆网络(LSTM)、Transformer 和 BERT 等,它们在近年来显著提升了 NLP 任务的性能。 通过阅读本书,读者可以学习到如何使用 Python 库,如 NLTK(Natural Language Toolkit)、spaCy、TextBlob 和 Gensim 等,来实现这些 NLP 技术。此外,书中可能会包含实际案例和练习,帮助读者巩固所学并将其应用到实际项目中。 "Natural Language Processing with Python" 是一个全面的指南,对于那些想要进入 NLP 领域或提升现有技能的人来说,是一本不可多得的资源。
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Python Natural Language Processing by Jalaj Thanaki English | 31 July 2017 | ISBN: 1787121429 | ASIN: B072B8YWCJ | 486 Pages | AZW3 | 11.02 MB Key Features Implement Machine Learning and Deep Learning techniques for efficient natural language processing Get started with NLTK and implement NLP in your applications with ease Understand and interpret human languages with the power of text analysis via Python Book Description This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world. What you will learn Focus on Python programming paradigms, which are used to develop NLP applications Understand corpus analysis and different types of data attribute. Learn NLP using Python libraries such as NLTK, Polyglot,
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