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Learn Natural Language Processing With Python Theory Notes

Download free Natural Language Processing with Python in PDF. This notes offers a highly accessible introduction to Natural language processing, the field That support verity of language, technologies, from predictive text and email filtering to automatic summarization and translation.

In this notes you learn how to write python program that works with large collection of unstructured text. You’ll access richly annotated dataset using comprehensive rage of linguistic data structure. In this book you’ll easily understand the main algorithms for analyzing the content and structure of written communication.

This notes helps you to gain practical skills in neural language processing using the python programing language.  

Notes Neural Language Processing with Python
Language English
Type PDF
Size 5 MB
Addition With application Codes

You Learn These Topics From This Notes:  

1. Language Processing And Python

  • Computing with language: Text and Words
  • A Close Look at Python: Text at List of Words
  • Computing with Language: Simple Statistics
  • Back to Python: Making Decision and Taking Control 

2. Accessing Text Corpora And Lexical Resources

  • Accessing Text Corpora
  • conditional Frequency Distribution
  • More Python: Reusing Code
  • Lexical Resources
  • Word Net

3. Processing Raw Text

  • Accessing Text from Web and from Disk
  • String: Text Processing At the Lowest Level
  • Regular Expression for Detecting Word Pattern
  • Normalizing Text

4. Writing Structured Program

  • Back to the Basics
  • Sequence 
  • Question of Style
  • Doing More with Function
  • Program Development
  • Algorithm Design

5. Categorizing And Tagging Word

  • Using a Tagger
  • Tagged Corpora
  • Automatic Tagging
  • N-Gram Tagging
  • Transformation Based Tagging

6. Learning To Classify Text

  • Supervised Classification
  • Further Example Of Supervised Classification
  • Evaluation
  • Decision Trees
  • Naive Bayes Classifiers
  • Maximum Entropy Classifier

7. Extracting Information From Text

  • Information Extraction
  • Chunking
  • Developing and Evaluating Chunks
  • Named Entity Recognition

8. Analyzing Syntax Structured

  • Some Grammatical Dilemmas
  • What’s the Use of Syntax
  • Context-Free Grammar
  • Parsing with Context Free Grammar
  • Grammar Development

9. Building Feature Based Grammar

  • Grammatical Features
  • Processing Feature Structure
  • Extending a Feature Based Grammar

10. Analyzing The Meaning Of Syntax

  • Natural Language Understanding
  • Propositional Logic
  • First Order Logic
  • The Semantic of English Sentences
  • Discourse Semantics

11. Managing Linguistic Data

  • Corpus Structure: A Case Study
  • The Lifecycle of Corpus
  • Accruing Data
  • Working with XML
  • Working with Toolbox Data



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