Natural language processing: Python and NLTK: learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt
November 2016
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | vii, 682 Seiten Illustrationen, Diagramme |
| ISBN: | 9781787285101 |
Internformat
MARC
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| 245 | 1 | 0 | |a Natural language processing: Python and NLTK |b learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules |c authors: Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, Iti Mathur |
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Datensatz im Suchindex
| _version_ | 1804178191623913472 |
|---|---|
| adam_text | Module 1: NLTK Essentials
Chapter 1: Introduction to Natural Language Processing____________3
Why learn NLP? 4
Let s start playing with Pythonl 7
Diving into NLTK 13
Your turn 19
Summary 19
Chapter 2: Text Wrangling and Cleansing__________________________21
What is text wrangling? 21
Text cleansing 24
Sentence splitter 24
Tokenization 25
Stemming 26
Lemmatization 28
Stop word removal 28
Rare word removal 29
Spell correction 30
Your turn 30
Summary 31
Chapter 3: Part of Speech Tagging________________________________33
What is Part of speech tagging 33
Named Entity Recognition (NER) 42
Your Turn 44
Summary 45
[i]
Table of Contents
Chapter 4: Parsing Structure in Text______________________________47
Shallow versus deep parsing 48
The two approaches in parsing 48
Why we need parsing 48
Different types of parsers 50
Dependency parsing 52
Chunking 54
Information extraction 57
Summary 60
Chapter 5: NLP Applications ______________________________________61
Building your first NLP application 62
Other NLP applications 65
Summary 74
Chapter 6: Text Classification____________________________________75
Machine learning 76
Text classification 77
Sampling 79
The Random forest algorithm 89
Text clustering 89
Topic modeling in text 91
References 93
Summary 94
Chapter 7: Web Crawling__________________________________________ 95
Web crawlers 95
Writing your first crawler 96
Data flow in Scrapy 99
The Sitemap spider 107
The item pipeline 108
External references 110
Summary 110
Chapter 8: Using NLTK with Other Python Libraries________________111
NumPy 112
SciPy 120
pandas 126
matplotlib 132
External references 137
Summary 137
Chapter 9: Social Media Mining in Python 139
Data collection 140
m
Table of Contents
Data extraction 144
Geovisualization 146
Summary 155
Chapter 10: Text Mining at Scale_______________________________157
Different ways of using Python on Hadoop 158
NLTKonHadoop 159
Scikit-learn on Hadoop 163
PySpark 167
Summary 169
Module 2: Python 3 Text Processing with NLTK 3
Cookbook
Chapter 1: Tokenizing Text and WordNet Basics______________________173
Introduction 173
Tokenizing text into sentences 174
Tokenizing sentences into words 176
Tokenizing sentences using regular expressions 178
Training a sentence tokenizer 180
Filtering stopwords in a tokenized sentence 182
Looking up Synsets for a word in WordNet 184
Looking up lemmas and synonyms in WordNet 186
Calculating WordNet Synset similarity 189
Discovering word collocations 191
Chapter 2: Replacing and Correcting Words__________________________195
Introduction 195
Stemming words 196
Lemmatizing words with WordNet 198
Replacing words matching regular expressions 200
Removing repeating characters 203
Spelling correction with Enchant 205
Replacing synonyms 209
Replacing negations with antonyms 212
Chapter 3: Creating Custom Corpora_________________________________215
Introduction 215
Setting up a custom corpus 216
Creating a wordlist corpus 218
Creating a part-of-speech tagged word corpus 221
Creating a chunked phrase corpus 225
[m]
Table of Contents
Creating a categorized text corpus 230
Creating a categorized chunk corpus reader 232
Lazy corpus loading 239
Creating a custom corpus view 241
Creating a MongoDB-backed corpus reader 245
Corpus editing with file locking 248
Chapter 4: Part-of-speech Tagging__________________________________251
Introduction 251
Default tagging 252
Training a unigram part-of-speech tagger 255
Combining taggers with backoff tagging 258
Training and combining ngram taggers 260
Creating a model of likely word tags 263
Tagging with regular expressions 265
Affix tagging 266
Training a Brill tagger 268
Training the TnT tagger 271
Using WordNet for tagging 273
Tagging proper names 276
Classifier-based tagging 277
Training a tagger with NLTK-Trainer 280
Chapter 5: Extracting Chunks_______________________________________289
Introduction 289
Chunking and chinking with regular expressions 290
Merging and splitting chunks with regular expressions 296
Expanding and removing chunks with regular expressions 299
Partial parsing with regular expressions 302
Training a tagger-based chunker 305
Classification-based chunking 309
Extracting named entities 313
Extracting proper noun chunks 315
Extracting location chunks 317
Training a named entity chunker 320
Training a chunker with NLTK-Trainer 322
Chapter 6: Transforming Chunks and Trees _________________________ 329
Introduction 329
Filtering insignificant words from a sentence 330
Correcting verb forms 332
Swapping verb phrases 335
Swapping noun cardinals 336
[iv]
Table of Contents
Swapping infinitive phrases 338
Singularizing plural nouns 339
Chaining chunk transformations 340
Converting a chunk tree to text 342
Flattening a deep tree 343
Creating a shallow tree 347
Converting tree labels 349
Chapter 7: Text Classification__________________________________________353
Introduction 353
Bag of words feature extraction 354
Training a Naive Bayes classifier 357
Training a decision tree classifier 363
Training a maximum entropy classifier 367
Training scikit-learn classifiers 371
Measuring precision and recall of a classifier 376
Calculating high information words 380
Combining classifiers with voting 385
Classifying with multiple binary classifiers 387
Training a classifier with NLTK-Trainer 394
Chapter 8: Distributed Processing and Handling Large Datasets 403
Introduction 403
Distributed tagging with execnet 404
Distributed chunking with execnet 408
Parallel list processing with execnet 410
Storing a frequency distribution in Redis 413
Storing a conditional frequency distribution in Redis 417
Storing an ordered dictionary in Redis 419
Distributed word scoring with Redis and execnet 423
Chapter 9: Parsing Specific Data Types_________________________________ 429
Introduction 429
Parsing dates and times with dateutil 430
Timezone lookup and conversion 432
Extracting URLs from HTML with Ixml 435
Cleaning and stripping HTML 437
Converting HTML entities with BeautifuiSoup 438
Detecting and converting character encodings 440
Chapter 10: Penn Treebank Part-of-speech Tags___________________________443
tv]
Table of Contents
Module 3: Mastering Natura! Language
Processing with Python
Chapter 1: Working with Strings_________________________________ 447
Tokenization 447
Normalization 454
Substituting and correcting tokens 456
Applying Zipf s law to text 461
Similarity measures 462
Summary 467
Chapter 2: Statistical Language Modeling________________________ 469
Understanding word frequency 469
Applying smoothing on the MLE model 482
Develop a back-off mechanism for NILE 490
Applying interpolation on data to get mix and match 490
Evaluate a language model through perplexity 491
Applying metropolis hastings in modeling languages 491
Applying Gibbs sampling in language processing 491
Summary 494
Chapter 3: Morphology - Getting Our Feet Wet_____________________495
Introducing morphology 495
Understanding stemmer 496
Understanding lemmatization 499
Developing a stemmer for non-English language 500
Morphological analyzer 502
Morphological generator 504
Search engine 505
Summary 509
Chapter 4: Parts-of-Speech Tagging - Identifying Words___________511
Introducing parts-of-speech tagging 511
Creating POS-tagged corpora 517
Selecting a machine learning algorithm 519
Statistical modeling involving the n-gram approach 521
Developing a chunker using pos-tagged corpora 527
Summary 530
Chapter 5: Parsing ֊ Analyzing Training Data ____________________531
Introducing parsing 531
Treebank construction 532
Extracting Context Free Grammar (CFG) rules from Treebank 537
[Vi]
Table of Contents
Creating a probabilistic Context Free Grammar from CFG 543
CYK chart parsing algorithm 544
Earley chart parsing algorithm 546
Summary 552
Chapter 6: Semantic Analysis - Meaning Matters__________________553
Introducing semantic analysis 554
Generation of the synset id from Wordnet 570
Disambiguating senses using Wordnet 573
Summary 577
Chapter 7: Sentiment Analysis -1 Am Happy_______________________579
Introducing sentiment analysis 580
Summary 610
Chapter 8: Information Retrieval - Accessing Information________611
Introducing information retrieval 611
Vector space scoring and query operator interaction 622
Developing an IR system using latent semantic indexing 624
Text summarization 625
Question-answering system 627
Summary 628
Chapter 9: Discourse Analysis - Knowing Is Believing____________629
Introducing discourse analysis 629
Summary 644
Chapter 10: Evaluation of NLP Systems - Analyzing Performance 645
The need for evaluation of NLP systems 645
Evaluation of IR system 657
Metrics for error identification 658
Metrics based on lexical matching 659
Metrics based on syntactic matching 663
Metrics using shallow semantic matching 664
Summary 664
Bibliography____________________________________________________665
Index 667
[vii]
|
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| language | English |
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| spelling | Hardeniya, Nitin Verfasser (DE-588)1130605604 aut Natural language processing: Python and NLTK learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules authors: Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, Iti Mathur Birmingham ; Mumbai Packt November 2016 vii, 682 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Python (DE-588)118793772 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Sprachverarbeitung (DE-588)4116579-2 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 s Sprachverarbeitung (DE-588)4116579-2 s Computerlinguistik (DE-588)4035843-4 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Python (DE-588)118793772 p Perkins, Jacob Verfasser aut Chopra, Deepti Verfasser aut Joshi, Nisheeth Verfasser aut Mathur, Iti Verfasser (DE-588)1109098308 aut 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=030107865&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hardeniya, Nitin Perkins, Jacob Chopra, Deepti Joshi, Nisheeth Mathur, Iti Natural language processing: Python and NLTK learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules Python (DE-588)118793772 gnd Python Programmiersprache (DE-588)4434275-5 gnd Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Computerlinguistik (DE-588)4035843-4 gnd |
| subject_GND | (DE-588)118793772 (DE-588)4434275-5 (DE-588)4041354-8 (DE-588)4116579-2 (DE-588)4035843-4 |
| title | Natural language processing: Python and NLTK learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules |
| title_auth | Natural language processing: Python and NLTK learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules |
| title_exact_search | Natural language processing: Python and NLTK learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules |
| title_full | Natural language processing: Python and NLTK learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules authors: Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, Iti Mathur |
| title_fullStr | Natural language processing: Python and NLTK learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules authors: Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, Iti Mathur |
| title_full_unstemmed | Natural language processing: Python and NLTK learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules authors: Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, Iti Mathur |
| title_short | Natural language processing: Python and NLTK |
| title_sort | natural language processing python and nltk learn to build expert nlp and machine learning projects using nltk and other python libraries a course in three modules |
| title_sub | learn to build expert NLP and machine learning projects using NLTK and other Python libraries : a course in three modules |
| topic | Python (DE-588)118793772 gnd Python Programmiersprache (DE-588)4434275-5 gnd Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Computerlinguistik (DE-588)4035843-4 gnd |
| topic_facet | Python Python Programmiersprache Natürliche Sprache Sprachverarbeitung Computerlinguistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030107865&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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