Natural Language Processing With Python Analyzing Text With the Natural Language Toolkit 1st Edition by Steven Bird, Ewan Klein, Edward Loper 0596516495 9780596516499

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ISBN 10: 0596516495

ISBN 13: 9780596516499

Author: Steven Bird; Ewan Klein; Edward Loper

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you’ll learn how to write Python programs that work with large collections of unstructured text. You’ll access richly annotated datasets using a comprehensive range of linguistic data structures, and you’ll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify “named entities” Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you’re interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages — or if you’re simply curious to have a programmer’s perspective on how human language works — you’ll find Natural Language Processing with Python both fascinating and immensely useful.

Natural Language Processing With Python Analyzing Text With the Natural Language Toolkit 1st Table of contents:

1. Language Processing and Python

Computing with Language: Texts and Words

Getting Started with Python

Getting Started with NLTK

Searching Text

Counting Vocabulary

A Closer Look at Python: Texts as Lists of Words

Lists

Indexing Lists

Variables

Strings

Computing with Language: Simple Statistics

Frequency Distributions

Fine-Grained Selection of Words

Collocations and Bigrams

Counting Other Things

Back to Python: Making Decisions and Taking Control

Conditionals

Operating on Every Element

Nested Code Blocks

Looping with Conditions

Automatic Natural Language Understanding

Word Sense Disambiguation

Pronoun Resolution

Generating Language Output

Machine Translation

Spoken Dialogue Systems

Textual Entailment

Limitations of NLP

Summary

Further Reading

Exercises

2. Accessing Text Corpora and Lexical Resources

Accessing Text Corpora

Gutenberg Corpus

Web and Chat Text

Brown Corpus

Reuters Corpus

Inaugural Address Corpus

Annotated Text Corpora

Corpora in Other Languages

Text Corpus Structure

Loading Your Own Corpus

Conditional Frequency Distributions

Conditions and Events

Counting Words by Genre

Plotting and Tabulating Distributions

Generating Random Text with Bigrams

More Python: Reusing Code

Creating Programs with a Text Editor

Functions

Modules

Lexical Resources

Wordlist Corpora

A Pronouncing Dictionary

Comparative Wordlists

Shoebox and Toolbox Lexicons

WordNet

Senses and Synonyms

The WordNet Hierarchy

More Lexical Relations

Semantic Similarity

Summary

Further Reading

Exercises

3. Processing Raw Text

Accessing Text from the Web and from Disk

Electronic Books

Dealing with HTML

Processing Search Engine Results

Processing RSS Feeds

Reading Local Files

Extracting Text from PDF, MSWord, and Other Binary Formats

Capturing User Input

The NLP Pipeline

Strings: Text Processing at the Lowest Level

Basic Operations with Strings

Printing Strings

Accessing Individual Characters

Accessing Substrings

More Operations on Strings

The Difference Between Lists and Strings

Text Processing with Unicode

What Is Unicode?

Extracting Encoded Text from Files

Using Your Local Encoding in Python

Regular Expressions for Detecting Word Patterns

Using Basic Metacharacters

Ranges and Closures

Useful Applications of Regular Expressions

Extracting Word Pieces

Doing More with Word Pieces

Finding Word Stems

Searching Tokenized Text

Normalizing Text

Stemmers

Lemmatization

Regular Expressions for Tokenizing Text

Simple Approaches to Tokenization

NLTK’s Regular Expression Tokenizer

Further Issues with Tokenization

Segmentation

Sentence Segmentation

Word Segmentation

Formatting: From Lists to Strings

From Lists to Strings

Strings and Formats

Lining Things Up

Writing Results to a File

Text Wrapping

Summary

Further Reading

Exercises

4. Writing Structured Programs

Back to the Basics

Assignment

Equality

Conditionals

Sequences

Operating on Sequence Types

Combining Different Sequence Types

Generator Expressions

Questions of Style

Python Coding Style

Procedural Versus Declarative Style

Some Legitimate Uses for Counters

Functions: The Foundation of Structured Programming

Function Inputs and Outputs

Parameter Passing

Variable Scope

Checking Parameter Types

Functional Decomposition

Documenting Functions

Doing More with Functions

Functions As Arguments

Accumulative Functions

Higher-Order Functions

Named Arguments

Program Development

Structure of a Python Module

Multimodule Programs

Sources of Error

Debugging Techniques

Defensive Programming

Algorithm Design

Recursion

Space-Time Trade-offs

Dynamic Programming

A Sample of Python Libraries

Matplotlib

NetworkX

csv

NumPy

Other Python Libraries

Summary

Further Reading

Exercises

5. Categorizing and Tagging Words

Using a Tagger

Tagged Corpora

Representing Tagged Tokens

Reading Tagged Corpora

A Simplified Part-of-Speech Tagset

Nouns

Verbs

Adjectives and Adverbs

Unsimplified Tags

Exploring Tagged Corpora

Mapping Words to Properties Using Python Dictionaries

Indexing Lists Versus Dictionaries

Dictionaries in Python

Defining Dictionaries

Default Dictionaries

Incrementally Updating a Dictionary

Complex Keys and Values

Inverting a Dictionary

Automatic Tagging

The Default Tagger

The Regular Expression Tagger

The Lookup Tagger

Evaluation

N-Gram Tagging

Unigram Tagging

Separating the Training and Testing Data

General N-Gram Tagging

Combining Taggers

Tagging Unknown Words

Storing Taggers

Performance Limitations

Tagging Across Sentence Boundaries

Transformation-Based Tagging

How to Determine the Category of a Word

Morphological Clues

Syntactic Clues

Semantic Clues

New Words

Morphology in Part-of-Speech Tagsets

Summary

Further Reading

Exercises

6. Learning to Classify Text

Supervised Classification

Gender Identification

Choosing the Right Features

Document Classification

Part-of-Speech Tagging

Exploiting Context

Sequence Classification

Other Methods for Sequence Classification

Further Examples of Supervised Classification

Sentence Segmentation

Identifying Dialogue Act Types

Recognizing Textual Entailment

Scaling Up to Large Datasets

Evaluation

The Test Set

Accuracy

Precision and Recall

Confusion Matrices

Cross-Validation

Decision Trees

Entropy and Information Gain

Naive Bayes Classifiers

Underlying Probabilistic Model

Zero Counts and Smoothing

Non-Binary Features

The Naivete of Independence

The Cause of Double-Counting

Maximum Entropy Classifiers

The Maximum Entropy Model

Maximizing Entropy

Generative Versus Conditional Classifiers

Modeling Linguistic Patterns

What Do Models Tell Us?

Summary

Further Reading

Exercises

7. Extracting Information from Text

Information Extraction

Information Extraction Architecture

Chunking

Noun Phrase Chunking

Tag Patterns

Chunking with Regular Expressions

Exploring Text Corpora

Chinking

Representing Chunks: Tags Versus Trees

Developing and Evaluating Chunkers

Reading IOB Format and the CoNLL-2000 Chunking Corpus

Simple Evaluation and Baselines

Training Classifier-Based Chunkers

Recursion in Linguistic Structure

Building Nested Structure with Cascaded Chunkers

Trees

Tree Traversal

Named Entity Recognition

Relation Extraction

Summary

Further Reading

Exercises

8. Analyzing Sentence Structure

Some Grammatical Dilemmas

Linguistic Data and Unlimited Possibilities

Ubiquitous Ambiguity

What’s the Use of Syntax?

Beyond n-grams

Context-Free Grammar

A Simple Grammar

Writing Your Own Grammars

Recursion in Syntactic Structure

Parsing with Context-Free Grammar

Recursive Descent Parsing

Shift-Reduce Parsing

The Left-Corner Parser

Well-Formed Substring Tables

Dependencies and Dependency Grammar

Valency and the Lexicon

Scaling Up

Grammar Development

Treebanks and Grammars

Pernicious Ambiguity

Weighted Grammar

Summary

Further Reading

Exercises

9. Building Feature-Based Grammars

Grammatical Features

Syntactic Agreement

Using Attributes and Constraints

Terminology

Processing Feature Structures

Subsumption and Unification

Extending a Feature-Based Grammar

Subcategorization

Heads Revisited

Auxiliary Verbs and Inversion

Unbounded Dependency Constructions

Case and Gender in German

Summary

Further Reading

Exercises

10. Analyzing the Meaning of Sentences

Natural Language Understanding

Querying a Database

Natural Language, Semantics, and Logic

Propositional Logic

First-Order Logic

Syntax

First-Order Theorem Proving

Summarizing the Language of First-Order Logic

Truth in Model

Individual Variables and Assignments

Quantification

Quantifier Scope Ambiguity

Model Building

The Semantics of English Sentences

Compositional Semantics in Feature-Based Grammar

The λ-Calculus

Quantified NPs

Transitive Verbs

Quantifier Ambiguity Revisited

Discourse Semantics

Discourse Representation Theory

Discourse Processing

Summary

Further Reading

Exercises

11. Managing Linguistic Data

Corpus Structure: A Case Study

The Structure of TIMIT

Notable Design Features

Fundamental Data Types

The Life Cycle of a Corpus

Three Corpus Creation Scenarios

Quality Control

Curation Versus Evolution

Acquiring Data

Obtaining Data from the Web

Obtaining Data from Word Processor Files

Obtaining Data from Spreadsheets and Databases

Converting Data Formats

Deciding Which Layers of Annotation to Include

Standards and Tools

Special Considerations When Working with Endangered Languages

Working with XML

Using XML for Linguistic Structures

The Role of XML

The ElementTree Interface

Using ElementTree for Accessing Toolbox Data

Formatting Entries

Working with Toolbox Data

Adding a Field to Each Entry

Validating a Toolbox Lexicon

Describing Language Resources Using OLAC Metadata

What Is Metadata?

OLAC: Open Language Archives Community

Summary

Further Reading

Exercises

A. Afterword: The Language Challenge

Language Processing Versus Symbol Processing

Contemporary Philosophical Divides

NLTK Roadmap

Envoi…

B. Bibliography

NLTK Index

General Index

About the Authors

Colophon

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