Natural Language Processing Fundamentals: Build intelligent applications that can interpret the human language to deliver impactful results
By 作者: Sohom Ghosh - Dwight Gunning
ISBN-10 书号: 1789954045
ISBN-13 书号: 9781789954043
Release Finelybook 出版日期: 2019-03-30
pages 页数: (374 )
The Book Description
Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems.
If NLP hasn’t been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems.
You’ll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you’ll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you’ll understand how to apply NLP techniques to answer questions as can be used in chatbots.
By the end of this book, you’ll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
What you will learn
Obtain, verify, and clean data before transforming it into a correct format for use
Perform data analysis and machine learning tasks using Python
Understand the basics of computational linguistics
Build models for general natural language processing tasks
Evaluate the performance of a model with the right metrics
Visualize, quantify, and perform exploratory analysis from any text data
1 Introduction to Natural Language Processing
2 Basic Feature Extraction Methods
3 Developing a Text classifier
4 Collecting Text Data from the Web
5 Topic Modeling
6 Text Summarization and Text Generation
7 Vector Representation
8 Sentiment Analysis