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Text analytics with Python : a practical real-world approach to gaining actionable insights from your data /
Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Python teaches you both basic and advanced concepts, inclu...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[United States] :
Apress,
2016.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Sarkar, Dipanjan, |e author. | |
| 245 | 1 | 0 | |a Text analytics with Python : |b a practical real-world approach to gaining actionable insights from your data / |c Dipanjan Sarkar. |
| 264 | 1 | |a [United States] : |b Apress, |c 2016. | |
| 264 | 2 | |a New York, NY : |b Distributed to the Book trade worldwide by Springer | |
| 264 | 4 | |c ©2016 | |
| 300 | |a 1 online resource | ||
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| 505 | 0 | |a At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Natural Language Basics; Natural Language; What Is Natural Language?; The Philosophy of Language; Language Acquisition and Usage; Language Acquisition and Cognitive Learning; Language Usage; Linguistics; Language Syntax and Structure; Words; Phrases; Clauses; Grammar; Dependency grammars; Constituency Grammars; Word Order Typology; Language Semantics; Lexical Semantic Relations; Lemmas and Wordforms; Homonyms, Homographs, and Homophones; Heteronyms and Heterographs. | |
| 505 | 8 | |a PolysemesCapitonyms; Synonyms and Antonyms; Hyponyms and Hypernyms; Semantic Networks and Models; Representation of Semantics; Propositional Logic; First Order Logic; Text Corpora; Corpora Annotation and Utilities; Popular Corpora; Accessing Text Corpora; Accessing the Brown Corpus; Accessing the Reuters Corpus; Accessing the WordNet Corpus; Natural Language Processing; Machine Translation; Speech Recognition Systems; Question Answering Systems; Contextual Recognition and Resolution; Text Summarization; Text Categorization; Text Analytics; Summary; Chapter 2: Python Refresher. | |
| 505 | 8 | |a Getting to Know PythonThe Zen of Python; Applications: When Should You Use Python?; Drawbacks: When Should You Not Use Python?; Python Implementations and Versions; Installation and Setup; Which Python Version?; Which Operating System?; Integrated Development Environments; Environment Setup; Virtual Environments; Python Syntax and Structure; Data Structures and Types; Numeric Types; Strings; Lists; Sets; Dictionaries; Tuples; Files; Miscellaneous; Controlling Code Flow; Conditional Constructs; Looping Constructs; Handling Exceptions; Functional Programming; Functions; Recursive Functions. | |
| 505 | 8 | |a Anonymous FunctionsIterators; Comprehensions; Generators; The itertools and functools Modules; Classes; Working with Text; String Literals; String Operations and Methods; Basic Operations; Indexing and Slicing; Methods; Formatting; Regular Expressions (Regexes); Text Analytics Frameworks; Summary; Chapter 3: Processing and Understanding Text; Text Tokenization; Sentence Tokenization; Word Tokenization; Text Normalization; Cleaning Text; Tokenizing Text; Removing Special Characters; Expanding Contractions; Case Conversions; Removing Stopwords; Correcting Words; Correcting Repeating Characters. | |
| 505 | 8 | |a Correcting SpellingsStemming; Lemmatization; Understanding Text Syntax and Structure; Installing Necessary Dependencies; Important Machine Learning Concepts; Parts of Speech (POS) Tagging; Recommended POS Taggers; Building Your Own POS Taggers; Shallow Parsing; Recommended Shallow Parsers; Building Your Own Shallow Parsers; Dependency-based Parsing; Recommended Dependency Parsers; Building Your Own Dependency Parsers; Constituency-based Parsing; Recommended Constituency Parsers; Building Your Own Constituency Parsers; Summary; Chapter 4: Text Classification; What Is Text Classification? | |
| 500 | |a Includes index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed January 3, 2017). | |
| 520 | |a Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. This book: Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Natural language processing (Computer science) | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Computer science. | |
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| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Data mining. | |
| 650 | 2 | |a Natural Language Processing | |
| 650 | 2 | |a Electronic Data Processing | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Traitement automatique des langues naturelles. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Informatique. | |
| 650 | 6 | |a Bases de données |x Gestion. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| 650 | 7 | |a Python (Computer program language) |2 fast | |
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