Natural language processing with Python and SpaCy: a practical introduction
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| Format: | Buch |
| Sprache: | English |
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San Francisco
No Starch Press
[2020]
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xviii, 192 Seiten Illustrationen, Diagramme |
| ISBN: | 9781718500525 |
Internformat
MARC
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| 245 | 1 | 0 | |a Natural language processing with Python and SpaCy |b a practical introduction |c by Yuli Vasiliev |
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| adam_text | CONTENTS IN DETAIL INTRODUCTION xv Using Python for Natural Language Processing....................................................................xvi The spaCy Library.................................................................................................................. xvi Who Should Read This Book?...............................................................................................xvii What s in the Book?............................................................................................................... xvii 1 HOW NATURAL LANGUAGE PROCESSING WORKS 1 How Can Computers Understand Language?.........................................................................2 Mapping Words and Numbers with Word Embedding.......................................... 2 Using Machine Learning for Natural Language Processing......................................3 Why Use Machine Learning for Natural Language Processing?............................ 5 What Is a Statistical Model in NLP?......................................................................................... 8 Neural Network Models........................................................................................... 9 Convolutional Neural Networks for NLP............................................................... 11 What Is Still on You.............................................................................................................. 12 Keywords................................................................................................................ 12
Context.................................................................................................................. 13 Meaning Transition................................................................................................ 13 Summary.............................................................................................................................. 14 2 THE TEXT-PROCESSING PIPELINE 15 Setting Up Your Working Environment............................................................................... 16 Installing Statistical Models for spaCy.................................................................................. 16 Basic NLP Operations with spaCy............................................................ 17 Tokenization ......................................................................................................... 18 Lemmatization....................................................................................................... 18 Applying Lemmatization for Meaning Recognition..................................................19 Part-of-Speech Tagging........................................................................................... 21 Using Part-of-Speech Tags to Find Relevant Verbs............ ................................. 23 Context Is Important................................................................................................ 24 Syntactic Relations.................................................................................................. 24 Try
This........................................................................................................ 28 Named Entity Recognition.......................................................................................29 Summary.................................................................................................................................29
3 WORKING WITH CONTAINER OBJECTS AND CUSTOMIZING SPACY 31 spaCy s Container Objects.....................................................................................................31 Getting the Index of a Token in a Doc Object........................................................ 32 Iterating over a Token s Syntactic Children............................................................. 33 The doc.sents Container........................................................................................... 33 The doc.noun_chunks Container............................................................................. 35 Try This..................................................................................................................... 35 The Span Object....................................................................................................... 36 Try This..................................................................................................................... 37 Customizing the Text-Processing Pipeline ............................................................................. 37 Disabling Pipeline Components ............................................................................. 38 Loading a Model Step by Step................................................................................38 Customizing the Pipeline Components.................................................................... 40 Using spaCy s С-Level Data Structures ... *.........................................................................42 How It
Works............................................................................................................43 Preparing Your Working Environment and Getting Text Files...............................43 Your Cython Script................................................................................ Building a Cython Module................................................................. Testing the Module.................................................................................................. 45 Summary................................................................................................................................. 46 4 EXTRACTING AND USING LINGUISTIC FEATURES 47 Extracting and Generating Text with Part-of-Speech Tags....................................................48 Numeric, Symbolic, and Punctuation Tags............................................................. 48 Extracting Descriptions of Money........................................................................... 49 Try This.....................................................................................................................50 Turning Statements into Questions........................................................................... 51 Try This..................................................................................................................... 54 Using Syntactic Dependency Labels in Text Processing........................................................ 55 Distinguishing Subjects from
Objects...................................................................... 55 Deciding What Question a Chatbot Should Ask.................................................... 56 Try This..................................................................................................................... 60 Summary................................................................................................................................. 61 5 WORKING WITH WORD VECTORS 63 Understanding Word Vectors................................................................................................ 64 Defining Meaning with Coordinates.......................................................................64 Using Dimensions to Represent Meaning............................................................... 65 The Similarity Method.............................................................................................. 66 Choosing Keywords for Semantic Similarity Calculations......................................67 Installing Word Vectors..........................................................................................................68 Taking Advantage of Word Vectors That Come with spaCy Models................... 68 Using Third-Party Word Vectors..............................................................................68 Comparing spaCy Objects..................................................................................................... 69 Using Semantic Similarity for Categorization Tasks............................................... 70 Extracting
Nouns as a Preprocessing Step............................................................. 71 X Contents in Detail 44 45
Try This..........................................................................................................................72 Extracting and Comparing Named Entities................................................................. 72 Summary............... !.................................................................................................................... 74 6 FINDING PATTERNS AND WALKING DEPENDENCY TREES 75 Word Sequence Patterns ................................................................................................... Finding Patterns Based on Linguistic Features......................................................... 76 Try This............................................................................................................................ 77 Checking an Utterance for a Pattern........................................................................... 77 Using spaCy s Matcher to Find Word Sequence Patterns..................................... 79 Applying Several Patterns............................................................................................80 Creating Patterns Based on Customized Features.....................................................81 Choosing Which Patterns to Apply............................................................................. 83 Using Word Sequence Patterns in Chatbots to Generate Statements..................83 Try This............................................................................................................................ 86 Extracting Keywords from Syntactic
Dependency Trees............................................................ 86 Walking a Dependency Tree for Information Extraction.......................................... 87 Iterating over the Heads of Tokens.............................................................................87 Condensing a Text Using Dependency Trees........................................................... 89 Try This............................................................................................................................ 91 Using Context to Improve the Ticket-Booking Chatbot.............................................................. 91 Making a Smarter Chatbot by Finding Proper Modifiers....................................................... 94 Summary......................................................................................................................................... 95 7 VISUALIZATIONS 76 97 Getting Started with spaCy s Built-In Visualizers.........................................................................98 displaCy Dependency Visualizer.................................................... displaCy Named Entity Visualizer..................................................... Visualizing from Within spaCy............................................................................................... 100 Visualizing Dependency Parsing............................................................................... 100 Try This......................................................................................................................... 102
Sentence-by-Sentence Visualizations........................................................................ 102 Customizing Your Visualizations with the Options Argument............................................. 103 Using Dependency Visualizer Options..................................................................... 103 Try This......................................................................................................................... 104 Using Named Entity Visualizer Options................................................................... 104 Exporting a Visualization to a File.......................................................................................... 106 Using displaCy to Manually Render Data............................................................................... 107 Formatting the Data...................................................................................... Try This....................................................................... Summary.................................................................................................................................... 109 98 99 108 8 INTENT RECOGNITION 111 Extracting the Transitive Verb and Direct Object for Intent Recognition............................ 112 Obtaining the Transitive Verb/Direct Object Pair................................................. 113 Extracting Multiple Intents with token.conjuncts...................................................... 113 Contents in Detail XI
Try This........................................................................................................................ Using Word Lists to Extract the Intent...................................................................... Finding the Meanings of Words Using Synonyms and Semantic Similarity.................... Recognizing Synonyms Using Predefined Lists ..................................................... . Try This........................................................................................................................ Recognizing Implied Intents Using Semantic Similarity ...................................... Try This........................................................................................................................ Extracting Intent from a Sequence of Sentences................................................................... Walking the Dependency Structures of a Discourse ........................................... Replacing Proforms with Their Antecedents............................................................ Try This........................................................................................................................ Summary.................................................................................................................................... 9 STORING USER INPUT IN A DATABASE ‘ « Converting Unstructured Data into Structured Data.............................................................. Extracting Data into Interchange
Formats.............................................................. Moving Application Logic to the Database............................................................ Building a Database-Powered Chatbot.................................................................................. Gathering the Data and Building a JSON Object ................................................ Converting Number Words to Numbers................................................................. Preparing Your Database Environment................................................................. Sending Data to the Underlying Database............................................................ When a User s Request Doesn t Contain Enough Information............................ Try This........................................................................................................................ Summary..................................................................................................................................... 115 115 117 117 119 120 121 122 122 122 125 125 127 128 129 130 132 132 133 135 137 138 139 139 10 TRAINING MODELS 141 Training a Model s Pipeline Component.................................................................................. Training the Entity Recognizer................................................................................................... Deciding Whether You Need to Train the Entity Recognizer.............................. Creating Training
Examples....................................................................................... Automating the Example Creation Process.............................................................. Disabling the Other Pipeline Components.............................................................. The Training Process................................................................................................... Evaluating the Updated Recognizer.......................................................................... Creating a New Dependency Parser....................................................................................... Custom Syntactic Parsing to Understand User Input............................................... Deciding on Types of Semantic Relations to Use.................................................... Creating Training Examples....................................................................................... Training the Parser...................................................................................................... Testing Your Custom Parser....................................................................................... Try This.......................................................................................................................... Summary....................................................................................................................................... 142 143 143 * 144 144 146 146 148 149 149 150 150 152 152 153 153 xii Contents in Detail
11 DEPLOYING YOUR OWN CHATBOT 155 How Implementing and Deploying a Chatbot Works......................................................... Using Telegram as a Platform for Your Bot........................................................................... Creating a Telegram Account and Authorizing Your Bot................................... Getting Started with the python-telegram-bot Library.......................................... Using the telegram.ext Objects................................................................................ Creating a Telegram Chatbot That Uses spaCy.................................................... 156 157 157 158 159 160 Expanding the Chatbot............................................................................................ Holding the State of the Current Chat................................................................... Putting All the Pieces Together................................................................................ Try This....................................................................................................................... Summary.................................................................................................................................... 161 162 163 167 167 12 IMPLEMENTING WEB DATA AND PROCESSING IMAGES 169 How It Works.............................................................................................................................. Making Your Bot Find Answers to Questions from Wikipedia.......................................... Determining What the
Question Is About................................................................ Try This......................................................................................................................... Using Wikipedia to Answer User Questions........................................................... Try This......................................................................................................................... Reacting to Images Sent in a Chat.......................................................................................... Generating Descriptive Tags for Images Using Clarifai....................................... Using Tags to Generate Text Responses to Images................. ........................... Putting All the Pieces Together in a Telegram Bot................................................................... Importing the Libraries............................................................. Writing the Helper Functions.................................................................. Writing the Callback and mainj) Functions............................................................ Testing the Bot................................................................................. Try This................................................................................................. Summary.................................................................................................................................... 170 170 171 174 174 175 175 176 178 178 LINGUISTIC PRIMER 185 Dependency Grammars vs. Phrase Structure
Grammars..................................................... Common Grammar Concepts................................................................................................. Transitive Verbs and Direct Objects........................................................................ Prepositional Objects............................................................................................... Modal Auxiliary Verbs . .......................................................................................... Personal Pronouns...................................................................................................... INDEX 178 178 180 181 182 183 185 187 187 187 188 188 189 Contents in Detail
|
| adam_txt |
CONTENTS IN DETAIL INTRODUCTION xv Using Python for Natural Language Processing.xvi The spaCy Library. xvi Who Should Read This Book?.xvii What's in the Book?. xvii 1 HOW NATURAL LANGUAGE PROCESSING WORKS 1 How Can Computers Understand Language?.2 Mapping Words and Numbers with Word Embedding. 2 Using Machine Learning for Natural Language Processing.3 Why Use Machine Learning for Natural Language Processing?. 5 What Is a Statistical Model in NLP?. 8 Neural Network Models. 9 Convolutional Neural Networks for NLP. 11 What Is Still on You. 12 Keywords. 12
Context. 13 Meaning Transition. 13 Summary. 14 2 THE TEXT-PROCESSING PIPELINE 15 Setting Up Your Working Environment. 16 Installing Statistical Models for spaCy. 16 Basic NLP Operations with spaCy. 17 Tokenization . 18 Lemmatization. 18 Applying Lemmatization for Meaning Recognition.19 Part-of-Speech Tagging. 21 Using Part-of-Speech Tags to Find Relevant Verbs. . 23 Context Is Important. 24 Syntactic Relations. 24 Try
This. 28 Named Entity Recognition.29 Summary.29
3 WORKING WITH CONTAINER OBJECTS AND CUSTOMIZING SPACY 31 spaCy's Container Objects.31 Getting the Index of a Token in a Doc Object. 32 Iterating over a Token's Syntactic Children. 33 The doc.sents Container. 33 The doc.noun_chunks Container. 35 Try This. 35 The Span Object. 36 Try This. 37 Customizing the Text-Processing Pipeline . 37 Disabling Pipeline Components . 38 Loading a Model Step by Step.38 Customizing the Pipeline Components. 40 Using spaCy's С-Level Data Structures . *.42 How It
Works.43 Preparing Your Working Environment and Getting Text Files.43 Your Cython Script. Building a Cython Module. Testing the Module. 45 Summary. 46 4 EXTRACTING AND USING LINGUISTIC FEATURES 47 Extracting and Generating Text with Part-of-Speech Tags.48 Numeric, Symbolic, and Punctuation Tags. 48 Extracting Descriptions of Money. 49 Try This.50 Turning Statements into Questions. 51 Try This. 54 Using Syntactic Dependency Labels in Text Processing. 55 Distinguishing Subjects from
Objects. 55 Deciding What Question a Chatbot Should Ask. 56 Try This. 60 Summary. 61 5 WORKING WITH WORD VECTORS 63 Understanding Word Vectors. 64 Defining Meaning with Coordinates.64 Using Dimensions to Represent Meaning. 65 The Similarity Method. 66 Choosing Keywords for Semantic Similarity Calculations.67 Installing Word Vectors.68 Taking Advantage of Word Vectors That Come with spaCy Models. 68 Using Third-Party Word Vectors.68 Comparing spaCy Objects. 69 Using Semantic Similarity for Categorization Tasks. 70 Extracting
Nouns as a Preprocessing Step. 71 X Contents in Detail 44 45
Try This.72 Extracting and Comparing Named Entities. 72 Summary. !. 74 6 FINDING PATTERNS AND WALKING DEPENDENCY TREES 75 Word Sequence Patterns . Finding Patterns Based on Linguistic Features. 76 Try This. 77 Checking an Utterance for a Pattern. 77 Using spaCy's Matcher to Find Word Sequence Patterns. 79 Applying Several Patterns.80 Creating Patterns Based on Customized Features.81 Choosing Which Patterns to Apply. 83 Using Word Sequence Patterns in Chatbots to Generate Statements.83 Try This. 86 Extracting Keywords from Syntactic
Dependency Trees. 86 Walking a Dependency Tree for Information Extraction. 87 Iterating over the Heads of Tokens.87 Condensing a Text Using Dependency Trees. 89 Try This. 91 Using Context to Improve the Ticket-Booking Chatbot. 91 Making a Smarter Chatbot by Finding Proper Modifiers. 94 Summary. 95 7 VISUALIZATIONS 76 97 Getting Started with spaCy's Built-In Visualizers.98 displaCy Dependency Visualizer. displaCy Named Entity Visualizer. Visualizing from Within spaCy. 100 Visualizing Dependency Parsing. 100 Try This. 102
Sentence-by-Sentence Visualizations. 102 Customizing Your Visualizations with the Options Argument. 103 Using Dependency Visualizer Options. 103 Try This. 104 Using Named Entity Visualizer Options. 104 Exporting a Visualization to a File. 106 Using displaCy to Manually Render Data. 107 Formatting the Data. Try This. Summary. 109 98 99 108 8 INTENT RECOGNITION 111 Extracting the Transitive Verb and Direct Object for Intent Recognition. 112 Obtaining the Transitive Verb/Direct Object Pair. 113 Extracting Multiple Intents with token.conjuncts. 113 Contents in Detail XI
Try This. Using Word Lists to Extract the Intent. Finding the Meanings of Words Using Synonyms and Semantic Similarity. Recognizing Synonyms Using Predefined Lists . . Try This. Recognizing Implied Intents Using Semantic Similarity . Try This. Extracting Intent from a Sequence of Sentences. Walking the Dependency Structures of a Discourse . Replacing Proforms with Their Antecedents. Try This. Summary. 9 STORING USER INPUT IN A DATABASE ‘ « Converting Unstructured Data into Structured Data. Extracting Data into Interchange
Formats. Moving Application Logic to the Database. Building a Database-Powered Chatbot. Gathering the Data and Building a JSON Object . Converting Number Words to Numbers. Preparing Your Database Environment. Sending Data to the Underlying Database. When a User's Request Doesn't Contain Enough Information. Try This. Summary. 115 115 117 117 119 120 121 122 122 122 125 125 127 128 129 130 132 132 133 135 137 138 139 139 10 TRAINING MODELS 141 Training a Model's Pipeline Component. Training the Entity Recognizer. Deciding Whether You Need to Train the Entity Recognizer. Creating Training
Examples. Automating the Example Creation Process. Disabling the Other Pipeline Components. The Training Process. Evaluating the Updated Recognizer. Creating a New Dependency Parser. Custom Syntactic Parsing to Understand User Input. Deciding on Types of Semantic Relations to Use. Creating Training Examples. Training the Parser. Testing Your Custom Parser. Try This. Summary. 142 143 143 * 144 144 146 146 148 149 149 150 150 152 152 153 153 xii Contents in Detail
11 DEPLOYING YOUR OWN CHATBOT 155 How Implementing and Deploying a Chatbot Works. Using Telegram as a Platform for Your Bot. Creating a Telegram Account and Authorizing Your Bot. Getting Started with the python-telegram-bot Library. Using the telegram.ext Objects. Creating a Telegram Chatbot That Uses spaCy. 156 157 157 158 159 160 Expanding the Chatbot. Holding the State of the Current Chat. Putting All the Pieces Together. Try This. Summary. 161 162 163 167 167 12 IMPLEMENTING WEB DATA AND PROCESSING IMAGES 169 How It Works. Making Your Bot Find Answers to Questions from Wikipedia. Determining What the
Question Is About. Try This. Using Wikipedia to Answer User Questions. Try This. Reacting to Images Sent in a Chat. Generating Descriptive Tags for Images Using Clarifai. Using Tags to Generate Text Responses to Images. . Putting All the Pieces Together in a Telegram Bot. Importing the Libraries. Writing the Helper Functions. Writing the Callback and mainj) Functions. Testing the Bot. Try This. Summary. 170 170 171 174 174 175 175 176 178 178 LINGUISTIC PRIMER 185 Dependency Grammars vs. Phrase Structure
Grammars. Common Grammar Concepts. Transitive Verbs and Direct Objects. Prepositional Objects. Modal Auxiliary Verbs . . Personal Pronouns. INDEX 178 178 180 181 182 183 185 187 187 187 188 188 189 Contents in Detail |
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| spelling | Vasiliev, Yuli Verfasser (DE-588)1050690826 aut Natural language processing with Python and SpaCy a practical introduction by Yuli Vasiliev San Francisco No Starch Press [2020] © 2020 xviii, 192 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Sprachverarbeitung (DE-588)4116579-2 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Natürliche Sprache (DE-588)4041354-8 s Sprachverarbeitung (DE-588)4116579-2 s DE-604 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032317151&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Vasiliev, Yuli Natural language processing with Python and SpaCy a practical introduction Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd |
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| title | Natural language processing with Python and SpaCy a practical introduction |
| title_auth | Natural language processing with Python and SpaCy a practical introduction |
| title_exact_search | Natural language processing with Python and SpaCy a practical introduction |
| title_exact_search_txtP | Natural language processing with Python and SpaCy a practical introduction |
| title_full | Natural language processing with Python and SpaCy a practical introduction by Yuli Vasiliev |
| title_fullStr | Natural language processing with Python and SpaCy a practical introduction by Yuli Vasiliev |
| title_full_unstemmed | Natural language processing with Python and SpaCy a practical introduction by Yuli Vasiliev |
| title_short | Natural language processing with Python and SpaCy |
| title_sort | natural language processing with python and spacy a practical introduction |
| title_sub | a practical introduction |
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