Natural Language Processing for Hackers: Learn to build awesome apps that can understand people

Natural Language Processing for Hackers: Learn to build awesome apps that can understand people
By 作者: George-Bogdan Ivanov
Publication Date 出版日期: 2019
ISBN: 9781617296567

File Size: 4918 KB
Print Pages 页数: 231 pages
Simultaneous Device Usage: Unlimited
Sold By 作者:Amazon Media EU S.à r.l.
Language 语言: English

The Book Description robot was collected from Amazon and arranged by Finelybook

Natural Language Processing (NLP) is a collection of techniques to analyze, interpret, and create human-understandable text and speech. Advances in machine learning have pushed NLP to new levels of accuracy and uncanny realism. Natural Language Processing for Hackers lays out everything you need to crawl, clean, build, fine-tune, and deploy natural language models from scratch—all with easy-to-read Python code.
Thanks to NLP, computers are capable of highly accurate text and speech-based interaction with humans. NLP capitalizes on powerful machine learning techniques that can detect patterns and extract meaning from human-generated text. As well as improving raw data processing, NLP technology is behind cutting edge UI developments such as chatbots and voice assistants that can process written and spoken commands and generate realistic and helpful responses.
Natural Language Processing for Hackers covers NLP end-to-end, giving you the skills and techniques that allow your computers to speak human. Unlike many research-oriented books that use the kind of clean datasets you would never find in the real world, this practical guide takes on NLP as you’ll actually use it. You’ll learn the key concepts of NLP by coding your own tools and projects, from a text analysis service right up to a full-featured chatbot. Everything is written in concise, easy-to-read Python code to ensure you’ll grok the most important aspects of Natural Language Processing. When you’re done, you will be able to apply the complete range of NLP techniques to build practical applications—even with messy real-world data.
What’s inside

Constructing your own Text Analysis engine
Building a Twitter listener that performs Sentiment Analysis on a certain subject
Assembling your own NLP toolbox, complete with Part Of Speech Tagger, Shallow Parser, Named Entity Extractor, and Dependency Parsers
Cleaning and standardising messy datasets


Part 1:Introduction to NLTK
NLTK Fundamentals
Getting started with Wordnet
Lemmatizing and Stemming
Part 2:Create a Text Analysis service
Introduction to Machine Learning
Getting Started with Scikit-Learn
Finding the data
Learning to Classify Text
Persisting models
Building the APl
Part 3:Create a Social Media Monitoring Service
Basics of Sentiment Analysis
Twitter Sentiment Data
Fine Tuning
Building the Twitter Listener
Classification Metrics
Multi-Class Metrics
Part 4:Build Your Own NLP Toolkit
Build Your Own Part-Of-Speech Tagger
Build a Chunker
Build a Named Entity Extractor
Build a Dependency Parser
Adding Labels to the Parser
Part 5:Build Your Own Chatbot Engine
General Architecture
Building the Core
MovieBot on Facebook


Natural Language Processing for Hackers

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