NaturalLanguageProcessing_PythonandNLTK - (EPUB全文下载)

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Natural Language Processing: Python and NLTK
Table of Contents
Natural Language Processing: Python and NLTK
Natural Language Processing: Python and NLTK
Credits
Preface
What this learning path covers
What you need for this learning path
Who this learning path is for
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Module 1
1. Introduction to Natural Language Processing
Why learn NLP?
Let's start playing with Python!
Lists
Helping yourself
Regular expressions
Dictionaries
Writing functions
Diving into NLTK
Your turn
Summary
2. Text Wrangling and Cleansing
What is text wrangling?
Text cleansing
Sentence splitter
Tokenization
Stemming
Lemmatization
Stop word removal
Rare word removal
Spell correction
Your turn
Summary
3. Part of Speech Tagging
What is Part of speech tagging
Stanford tagger
Diving deep into a tagger
Sequential tagger
N-gram tagger
Regex tagger
Brill tagger
Machine learning based tagger
Named Entity Recognition (NER)
NER tagger
Your Turn
Summary
4. Parsing Structure in Text
Shallow versus deep parsing
The two approaches in parsing
Why we need parsing
Different types of parsers
A recursive descent parser
A shift-reduce parser
A chart parser
A regex parser
Dependency parsing
Chunking
Information extraction
Named-entity recognition (NER)
Relation extraction
Summary
5. NLP Applications
Building your first NLP application
Other NLP applications
Machine translation
Statistical machine translation
Information retrieval
Boolean retrieval
Vector space model
The probabilistic model
Speech recognition
Text classification
Information extraction
Question answering systems
Dialog systems
Word sense disambiguation
Topic modeling
Language detection
Optical character recognition
Summary
6. Text Classification
Machine learning
Text classification
Sampling
Naive Bayes
Decision trees
Stochastic gradient descent
Logistic regression
Support vector machines
The Random forest algorithm
Text clustering
K-means
Topic modeling in text
Installing gensim
References
Summary
7. Web Crawling
Web crawlers
Writing your first crawler
Data flow in Scrapy
The Scrapy shell
Items
The Sitemap spider
The item pipeline
External references
Summary
8. Using NLTK with Other Python Libraries
NumPy
ndarray
Indexing
Basic operations
Extracting data from an array
Complex matrix operations
Reshaping and stacking
Random numbers
SciPy
Linear algebra
eigenvalues and eigenvectors
The sparse matrix
Optimization
pandas
Reading data
Series data
Column transform ............

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