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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 transformation
Noisy data
matplotlib
Subplot
Adding an axis
A scatter plot
A bar plot
3D plots
External references
Summary
9. Social Media Mining in Python
Data collection
Data extraction
Trending topics
Geovisualization
Influencers detection
Influencer friends
Summary
10. Text Mining at Scale
Different ways of using Python on Hadoop
Python streaming
Hive/Pig UDF
Streaming wrappers
NLTK on Hadoop
A UDF
Python streaming
Scikit-learn on Hadoop
PySpark
Summary
2. Module 2
1. Tokenizing Text and WordNet Basics
Introduction
Tokenizing text into sentences
Getting ready
How to do it...
How it works...
There's more...
Tokenizing sentences in other languages
See also
Tokenizing sentences into words
How to do it...
How it works...
There's more...
Separating contractions
PunktWordTokenizer
WordPunctTokenizer
See also
Tokenizing sentences using regular expressions
Getting ready
How to do it...
How it works...
There's more...
Simple whitespace tokenizer
See also
Training a sentence tokenizer
Getting ready
How to do it...
How it works...
There's more...
See also
Filtering stopwords in a tokenized sentence
Getting ready
How to do it...
How it works...
There's more...
See also
Looking up Synsets for a word in WordNet
Getting ready
How to do it...
How it works...
There's more...
Working with hypernyms
Part of speech (POS)
See also
Looking up lemmas and synonyms in WordNet
How to do it...
How it works...
There's more...
All possible synonyms
Antonyms
See also
Calculating WordNet Synset similarity
How to do it...
How it works...
There's more...
Comparing verbs
Path and Leacock Chordorow (LCH) similarity
See also
Discovering word collocations
Getting ready
How to do it...
How it works...
There's more...
Scoring functions
Scoring ngrams
See also
2. Replacing and Correcting Words
Introduction
Stemming words
How to do it...
How it works...
There's more...
The LancasterStemmer class
The RegexpStemmer class
The SnowballStemmer class
See also
Lemmatizing words with WordNet
Getting ready
How to do it...
How it works...
There's more...
Combining stemming with lemmatization
See also
Replacing words matching regular expressions
Getting ready
How to do it...
How it works...
There's more...
Replacement before tokenization
See also
Removing repeating characters
Getting ready
How to do it...
How it works...
There's more...
See also
Spelling correction with Enchant
Getting ready
How to do it...
How it works...
There's more...
The en_GB dictionary
Personal word lists
See also
Replacing synonyms
Getting ready
How to do it...
How it works...
There's more...
CSV synonym replacement
YAML synonym replacement
See also
Replacing negations with antonyms
How to do it...
How it works...
There's more...
See also
3. Creating Custom Corpora
Introduction
Setting up a custom corpus
Getting ready
How to do it...
How it works...
There's more...
Loading a YAML file
See also
Creating a wordlist corpus
Getting ready
How to do it...
How it works...
There's more...
Names wordlist corpus
English words corpus
See also
Creating a part-of-speech tagged word corpus
Getting ready
How to do it...
How it works...
There's more...
Customizing the word tokenizer
Customizing the sentence tokenizer
Customizing the paragraph block reader
Customizing the tag separator
Converting tags to a universal tagset
See also
Creating a chunked phrase corpus
Getting ready
How to do it...
How it works...
There's more...
Tree leaves
Treebank chunk corpus
CoNLL2000 corpus
See also
Creating a categorized text corpus
Getting ready
How to do it...
How it works...
There's more...
Category file
Categorized tagged corpus reader
Categorized corpora
See also
Creating a categorized chunk corpus reader
Getting ready
How to do it...
How it works...
There's more...
Categorized CoNLL chunk corpus reader
See also
Lazy corpus loading
How to do it...
How it works...
There's more...
Creating a custom corpus view
How to do it...
How it works...
There's more...
Block reader functions
Pickle corpus view
Concatenated corpus view
See also
Creating a MongoDB-backed corpus reader
Getting ready
How to do it...
How it works...
There's more...
See also
Corpus editing with file locking
Getting ready
How to do it...
How it works...
4. Part-of-speech Tagging
Introduction
Default tagging
Getting ready
How to do it...
How it works...
There's more...
Evaluating accuracy
Tagging sentences
Untagging a tagged sentence
See also
Training a unigram part-of-speech tagger
How to do it...
How it works...
There's more...
Overriding the context model
Minimum frequency cutoff
See also
Combining taggers with backoff tagging
How to do it...
How it works...
There's more...
Saving and loading a trained tagger with pickle
See also
Training and combining ngram taggers
Getting ready
How to do it...
How it works...
There's more...
Quadgram tagger
See also
Creating a model of likely word tags
How to do it...
How it works...
There's more...
See also
Tagging with regular expressions
Getting ready
How to do it...
How it works...
There's more...
See also
Affix tagging
How to do it...
How it works...
There's more...
Working with min_stem_length
See also
Training a Brill tagger
How to do it...
How it works...
There's more...
Tracing
See also
Training the TnT tagger
How to do it...
How it works...
There's more...
Controlling the beam search
Significance of capitalization
See also
Using WordNet for tagging
Getting ready
How to do it...
How it works...
See also
Tagging proper names
How to do it...
How it works...
See also
Classifier-based tagging
How to do it...
How it works...
There's more...
Detecting features with a custom feature detector
Setting a cutoff probability
Using a pre-trained classifier
See also
Training a tagger with NLTK-Trainer
How to do it...
How it works...
There's more...
Saving a pickled tagger
Training on a custom corpus
Training with universal tags
Analyzing a tagger against a tagged corpus
Analyzing a tagged corpus
See also
5. Extracting Chunks
Introduction
Chunking and chinking with regular expressions
Getting ready
How to do it...
How it works...
There's more...
Parsing different chunk types
Parsing alternative patterns
Chunk rule with context
See also
Merging and splitting chunks with regular expressions
How to do it...
How it works...
There's more...
Specifying rule descriptions
See also
Expanding and removing chunks with regular expressions
How to do it...
How it works...
There's more...
See also
Partial parsing with regular expressions
How to do it...
How it works...
There's more...
The ChunkScore metrics
Looping and tracing chunk rules
See also
Training a tagger-based chunker
How to do it...
How it works...
There's more...
Using different taggers
See also
Classification-based chunking
How to do it...
How it works...
There's more...
Using a different classifier builder
See also
Extracting named entities
How to do it...
How it works...
There's more...
Binary named entity extraction
See also
Extracting proper noun chunks
How to do it...
How it works...
There's more...
See also
Extracting location chunks
How to do it...
How it works...
There's more...
See also
Training a named entity chunker
How to do it...
How it works...
There's more...
See also
Training a chunker with NLTK-Trainer
How to do it...
How it works...
There's more...
Saving a pickled chunker
Training a named entity chunker
Training on a custom corpus
Training on parse trees
Analyzing a chunker against a chunked corpus
Analyzing a chunked corpus
See also
6. Transforming Chunks and Trees
Introduction
Filtering insignificant words from a sentence
Getting ready
How to do it...
How it works...
There's more...
See also
Correcting verb forms
Getting ready
How to do it...
How it works...
See also
Swapping verb phrases
How to do it...
How it works...
There's more...
See also
Swapping noun cardinals
How to do it...
How it works...
See also
Swapping infinitive phrases
How to do it...
How it works...
There's more...
See also
Singularizing plural nouns
How to do it...
How it works...
See also
Chaining chunk transformations
How to do it...
How it works...
There's more...
See also
Converting a chunk tree to text
How to do it...
How it works...
There's more...
See also
Flattening a deep tree
Getting ready
How to do it...
How it works...
There's more...
The cess_esp and cess_cat treebank
See also
Creating a shallow tree
How to do it...
How it works...
See also
Converting tree labels
Getting ready
How to do it...
How it works...
See also
7. Text Classification
Introduction
Bag of words feature extraction
How to do it...
How it works...
There's more...
Filtering stopwords
Including significant bigrams
See also
Training a Naive Bayes classifier
Getting ready
How to do it...
How it works...
There's more...
Classification probability
Most informative features
Training estimator
Manual training
See also
Training a decision tree classifier
How to do it...
How it works...
There's more...
Controlling uncertainty with entropy_cutoff
Controlling tree depth with depth_cutoff
Controlling decisions with support_cutoff
See also
Training a maximum entropy classifier
Getting ready
How to do it...
How it works...
There's more...
Megam algorithm
See also
Training scikit-learn classifiers
Getting ready
How to do it...
How it works...
There's more...
Comparing Naive Bayes algorithms
Training with logistic regression
Training with LinearSVC
See also
Measuring precision and recall of a classifier
How to do it...
How it works...
There's more...
F-measure
See also
Calculating high information words
How to do it...
How it works...
There's more...
The MaxentClassifier class with high information words
The DecisionTreeClassifier class with high information words
The SklearnClassifier class with high information words
See also
Combining classifiers with voting
Getting ready
How to do it...
How it works...
See also
Classifying with multiple binary classifiers
Getting ready
How to do it...
How it works...
There's more...
See also
Training a classifier with NLTK-Trainer
How to do it...
How it works...
There's more...
Saving a pickled classifier
Using different training instances
The most informative features
The Maxent and LogisticRegression classifiers
SVMs
Combining classifiers
High information words and bigrams
Cross-fold validation
Analyzing a classifier
See also
8. Distributed Processing and Handling Large Datasets
Introduction
Distributed tagging with execnet
Getting ready
How to do it...
How it works...
There's more...
Creating multiple channels
Local versus remote gateways
See also
Distributed chunking with execnet
Getting ready
How to do it...
How it works...
There's more...
Python subprocesses
See also
Parallel list processing with execnet
How to do it...
How it works...
There's more...
See also
Storing a frequency distribution in Redis
Getting ready
How to do it...
How it works...
There's more...
See also
Storing a conditional frequency distribution in Redis
Getting ready
How to do it...
How it works...
There's more...
See also
Storing an ordered dictionary in Redis
Getting ready
How to do it...
How it works...
There's more...
See also
Distributed word scoring with Redis and execnet
Getting ready
How to do it...
How it works...
There's more...
See also
9. Parsing Specific Data Types
Introduction
Parsing dates and times with dateutil
Getting ready
How to do it...
How it works...
There's more...
See also
Timezone lookup and conversion
Getting ready
How to do it...
How it works...
There's more...
Local timezone
Custom offsets
See also
Extracting URLs from HTML with lxml
Getting ready
How to do it...
How it works...
There's more...
Extracting links directly
Parsing HTML from URLs or files
Extracting links with XPaths
See also
Cleaning and stripping HTML
Getting ready
How to do it...
How it works...
There's more...
See also
Converting HTML entities with BeautifulSoup
Getting ready
How to do it...
How it works...
There's more...
Extracting URLs with BeautifulSoup
See also
Detecting and converting character encodings
Getting ready
How to do it...
How it works...
There's more...
Converting to ASCII
UnicodeDammit conversion
See also
A. Penn Treebank Part-of-speech Tags
3. Module 3
1. Working with Strings
Tokenization
Tokenization of text into sentences
Tokenization of text in other languages
Tokenization of sentences into words
Tokenization using TreebankWordTokenizer
Tokenization using regular expressions
Normalization
Eliminating punctuation
Conversion into lowercase and uppercase
Dealing with stop words
Calculate stopwords in English
Substituting and correcting tokens
Replacing words using regular expressions
Example of the replacement of a text with another text
Performing substitution before tokenization
Dealing with repeating characters
Example of deleting repeating characters
Replacing a word with its synonym
Example of substituting word a with its synonym
Applying Zipf's law to text
Similarity measures
Applying similarity measures using Ethe edit distance algorithm
Applying similarity measures using Jaccard's Coefficient
Applying similarity measures using the Smith Waterman distance
Other string similarity metrics
Summary
2. Statistical Language Modeling
Understanding word frequency
Develop MLE for a given text
Hidden Markov Model estimation
Applying smoothing on the MLE model
Add-one smoothing
Good Turing
Kneser Ney estimation
Witten Bell estimation
Develop a back-off mechanism for MLE
Applying interpolation on data to get mix and match
Evaluate a language model through perplexity
Applying metropolis hastings in modeling languages
Applying Gibbs sampling in language processing
Summary
3. Morphology – Getting Our Feet Wet
Introducing morphology
Understanding stemmer
Understanding lemmatization
Developing a stemmer for non-English language
Morphological analyzer
Morphological generator
Search engine
Summary
4. Parts-of-Speech Tagging – Identifying Words
Introducing parts-of-speech tagging
Default tagging
Creating POS-tagged corpora
Selecting a machine learning algorithm
Statistical modeling involving the n-gram approach
Developing a chunker using pos-tagged corpora
Summary
5. Parsing – Analyzing Training Data
Introducing parsing
Treebank construction
Extracting Context Free Grammar (CFG) rules from Treebank
Creating a probabilistic Context Free Grammar from CFG
CYK chart parsing algorithm
Earley chart parsing algorithm
Summary
6. Semantic Analysis – Meaning Matters
Introducing semantic analysis
Introducing NER
A NER system using Hidden Markov Model
Training NER using Machine Learning Toolkits
NER using POS tagging
Generation of the synset id from Wordnet
Disambiguating senses using Wordnet
Summary
7. Sentiment Analysis – I Am Happy
Introducing sentiment analysis
Sentiment analysis using NER
Sentiment analysis using machine learning
Evaluation of the NER system
Summary
8. Information Retrieval – Accessing Information
Introducing information retrieval
Stop word removal
Information retrieval using a vector space model
Vector space scoring and query operator interaction
Developing an IR system using latent semantic indexing
Text summarization
Question-answering system
Summary
9. Discourse Analysis – Knowing Is Believing
Introducing discourse analysis
Discourse analysis using Centering Theory
Anaphora resolution
Summary
10. Evaluation of NLP Systems – Analyzing Performance
The need for evaluation of NLP systems
Evaluation of NLP tools (POS taggers, stemmers, and morphological analyzers)
Parser evaluation using gold data
Evaluation of IR system
Metrics for error identification
Metrics based on lexical matching
Metrics based on syntactic matching
Metrics using shallow semantic matching
Summary
B. Bibliography
Index
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