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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.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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?