추가 적립 안내

Preface
Chapter 1: Corpus and WordNet
Chapter 2: Raw Text, Sourcing, and Normalization
Chapter 3: Pre-Processing
Chapter 4: Regular Expressions
Chapter 5: POS Tagging and Grammars
Chapter 6: Chunking, Sentence Parse, and Dependencies
Chapter 7: Information Extraction and Text Classification
Chapter 8: Advanced LNP Recipes
Chapter 9: Applications of Deep Learning in LNP
Chapter 10: Advanced Applications of Deep Learning in NLP
Index

Key Features
Independent recipes that will teach you how to efficiently perform natural language processing in Python
Use dictionaries to create your own named entities using this easy-to-follow guide
Learn how to implement NLTK for various scenarios with the help of example-rich recipes that lets you go beyond basic natural language processing

Book Description

Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.

This book will include unique recipes that will teach you various aspects of performing natural language processing with NLTK-the leading Python platform for the task. You will come across various recipes during the course, including natural language understanding, natural language processing, and syntactic analysis. You will learn how to understand the language, plan the sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures, lexical analysis, syntactic and semantic analysis, pragmatic analysis, and application of deep learning techniques.

By the end of this book, you will have all the knowledge to implement natural language processing with Python.

What you will learn
Explore corpus management using internal and external corpora
Learn Wordnet usage and a couple of simple application assignment using wordnet
Discover the steps to operate upon raw text
Learn to do Tokenisation, Stemming, Lemmatizatio, Spelling corrections, Stop words removals and more
Understand regular expressions for pattern matching
Learn to use and write your own POS taggers and Grammars
Learn to evaluate your own trained models
Explore Deep Learning techniques in NLP.
Generate Text from Nietzsche's writing using LSTM
Utilize BABI dataset & LSTM to model the episode