Natural language processing in action: understanding, analyzing, and generating text with Python
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Shelter Island
Manning
[2019]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxix, 512 Seiten Illustrationen |
| ISBN: | 1617294632 9781617294631 |
Internformat
MARC
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Datensatz im Suchindex
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|---|---|
| adam_text | contents foreword xiii preface xv acknowledgments xxi about this book xxiv about the authors xxvii about the cover illustration Part 1 xxix Wordy machines............................ ...... ..............1 J Packets of thought (NLP overview) 1.1 1.2 Natural language vs. programming language The magic 4 Machines that converse 1.3 1.4 3 5 ■ The math 6 Practical applications 8 Language through a computer’s “eyes” 9 The language of locks 10 ■ Regular expressions A simple chatbot 12 · Another way 16 1.5 1.6 1.7 1.8 1.9 A brief overflight of hyperspace 19 Word order and grammar 21 A chatbot natural language pipeline 22 Processing in depth 25 Natural language IQ 27 VII 4 11
Vill CONTENTS Build your vocabulary (word tokenization) 2.1 2.2 30 Challenges (a preview of stemming) 32 Building your vocabulary with a tokenizer 33 Dot product 41 · Measuring bag-of-words overlap 42 A tokeii improvement 43 · Extending your vocabulary with n-grams 48 · Normalizing your vocabulary 54 2.3 Sentiment 62 VADER—A rule-based sentiment analyzer Math with words (TF-IDF vectors) 3.1 3.2 65 70 Bag of words 71 Vectorizing 76 Vector spaces 3.3 3.4 64 · Naive Bayes 19 Zipf s Law 83 Topic modeling 86 Return of Zipf 89· Relevance ranking 90· Tools 93 Alternatives 93 · Okapi BM25 95 · What’s next 95 /Í Finding meaning in word counts (semantic analysis) 4.1 From word counts to topic scores 97 98 TF-IDF vectors and lemmatization 99· Topic vectors 99 Thought experiment 101 · An algorithm for scoring topics An LDA classifier 107 4.2 Latent semantic analysis 111 Your thought experiment made real 4.3 Singular value decomposition 113 116 U—left singular vectors 118 · S—singular values 119 VT—right singular vectors 120· SVD matrix orientation Truncating the topics 121 4.4 105 Principal component analysis 120 123 PCA on 3D vectors 125 · Stop horsing around and get back to NLP 126· Using PCA for SMS message semantic analysis 128 Using truncated SVD for SMS message semantic analysis 130 How well does LSA work for spam classification ? 131 4.5 Latent Dirichlet allocation (LDiA) 134 The LDiA idea 135 · LDiA topic modelfor SMS messages LDiA + LDA = spam classifier 140 · A fairer comparison: 32 LDiA topics 142 137
ІХ CONTENTS 4.6 4.7 Distance and similarity Steering with feedback 143 146 Linear discriminant analysis 4.8 Topic vector power Semantic search Part 2 147 148 150 · Improvements 152 Deeper learning (neural networks) ...... 153 Baby steps with neural networks (perceptrons and backpropagation) 155 5.1 Neural networks, the ingredient list 156 Perceptron 157 · A numerical perceptron 157 · Detour through bias 158 · Let’s go skiing—the error surface 172 Off the chair lift, onto the slope 173 · Let’s shake things up a bit 174 · Keras: neural networks in Python 175 · Onward and deepward 179 · Normalization: input with style 179 Reasoning with word vectors (Word2vec) 6.1 Semantic queries and analogies Analogy questions 6.2 Word vectors 181 182 183 184 Vector-oriented reasoning 187 · How to compute Word2vec representations 191 · How to use the gensim.word2vec module 200 · How to generate your own word vector representations 202· Word2vec vs. GloVe (Global Vectors) 205 fastText 205 · Word2vec vs. LSA 206 · Visualizing word relationships 207 · Unnatural words 214 · Document similarity with Doc2vec 215 J Getting words in order with convolutional neural networks 1 (CNNs) 218 7.1 7.2 7.3 Learning meaning 220 Toolkit 221 Convolutional neural nets 222 Building blocks 223 · Step size (stride) 224 · Filter composition 224 · Padding 226 · Learning 228 7.4 Narrow windows indeed 228 Implementation in Keras: prepping the data 230 ■ Convolutional neural network architecture 235 ■ Pooling 236 Dropout 238· The cherry on the sundae 239· Let’s get to
CONTENTS learning (training) 241 · Using the model in a pipeline Where do you gofrom herei 244 Loopy (recurrent) neural networks (RNNs) 8.1 243 247 Remembering with recurrent networks 250 Backpropagation through time 255 · When do we update what? 257 · Recap 259 · There’s always a catch 259 Recurrent neural net with Keras 260 8.2 8.3 8.4 8.5 Putting things together 264 Let’s get to learning our past selves Hyperparameters 267 Predicting 269 Statefulness 270 · Two-way street 266 271 · What is this thing? Improving retention with long short-term memory networks 9.1 LSTM 275 Backpropagation through time 284 · Where does the rubber hit the road? 287 · Dirty data 288 · Back to the dirty data 291 Words are hard. Letters are easier. 292 · My turn to chat 298 My turn to speak more clearly 300 · Learned how to say, but not yet what 308 · Other kinds of memory 308 · Going deeper 309 Sequence-to-sequence models and attention 10.1 Encoder-decoder architecture 311 312 Decoding thought 313 · Look familiar? 315 · Sequence-tosequence conversation 316 · LSTM review 317 10.2 Assembling a sequence-to-sequence pipeline 318 Preparing your dataset for the sequence-to-sequence training Sequence-to-sequence model in Keras 320 · Sequence encoder 320 · Thought decoder 322 ■ Assembling the sequence-to-sequence network 323 10.3 Training the sequence-to-sequence network Generate output sequences 10.4 318 324 325 Building a chatbot using sequence-to-sequence networks 326 Preparing the corpus for your training 326 · Building your character dictionary 327 · Generate one-hot encoded training sets 328 ·
Train your sequence-to-sequence chatbot 329 272 274
Xl CONTENTS Assemble the model for sequence generation 330 ■ Predicting a sequence 330 ■ Generating a response 331 ■ Converse with your chatbot 331 10.5 Enhancements 332 Reduce training complexity with bucketing attention 333 10.6 ?лш 3 In the real world 332 ■ Paying 334 Getting real (real-world NLP CHALLENGES) a·«*·»*·»« »*«**··«« 337 Information extraction (named entity extraction and question answering) 339 11.1 11.2 Named entities and relations A knowledge base 340 Regular patterns 343 Regular expressions extraction 345 11.3 ■ 344 ■ 343 Information extraction as ML feature Information worth extracting Extracting GPS locations 11.4 339 Information extraction 347 346 ■ Extracting dates Extracting relationships (relations) 347 352 Part-of-speech (POS) tagging 353 ■ Entity name normalization 357 Relation normalization and extraction 358 * Word patterns 358 Segmentation 359 ■ Why won’t split( .!? ) work? 360 Sentence segmentation with regular expressions 361 11.5 12 In the real world 363 Getting chatty (dialog engines) 12.1 Language skill 366 Modern approaches 12.2 365 367 · A hybrid approach Pattern-matching approach 373 A pattern-matching chatbot with A1ML pattern matching 381 12.3 12.4 Grounding 382 Retrieval (search) 373 375 ■ A network view of 384 The context challenge 384 · Example retrieval-based chatbot 386 ■ A search-based chatbot 389
ХІІ CONTENTS 12.5 Generative models 391 Chat about NLPIA 12.6 Four-wheel drive 392 · Pros and cons of each approach 395 The Will to succeed 12.7 12.8 Design process Trickery 399 394 395 396 Ask questions with predictable answers 399 · Be entertaining When all ehe fails, search 400 · Being popular 400 · Be a connector 400 · Getting emotional 400 12.9 1 399 In the real world 401 Scaling up (optimization, parallelization, and batch processing) 403 13.1 13.2 Too much of a good thing (data) 404 Optimizing NLP algorithms 404 Indexing 405 · Advanced indexing 406 · Advanced indexing with Annoy 408 · Why use approximate indexes at аШ 412 An indexing workaround: discretizing 413 13.3 Constant RAM algorithms Gensim 13.4 414 414· Graph computing 415 Parallelizing your NLP computations 416 Training NLP models on GPUs 416· Renting vs. buying GPU rental options 418· Tensor processing units 419 13.5 Reducing the memory footprint during model training 419 13.6 Gaining model insights with TensorBoard How to visualize word embeddings 422 423 42 7 appendix A Your NLP tools appendix В Playful Python and regular expressions appendix C appendix D Vectors and matrices (linear algebra fundamentals) appendix E appendix F Machine Earning tools and techniques Setting up your A WS GPU 459 Locality sensitive hashing resources 481 glossary 490 index 497 473 434 446 440 417
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| author | Lane, Hobson Howard, Cole Hapke, Hannes Max |
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| isbn | 1617294632 9781617294631 |
| language | English |
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| spelling | Lane, Hobson Verfasser (DE-588)1190917785 aut Natural language processing in action understanding, analyzing, and generating text with Python Hobson Lane, Cole Howard, Hannes Max Hapke Shelter Island Manning [2019] © 2019 xxix, 512 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Automatische Sprachanalyse (DE-588)4129935-8 gnd rswk-swf Informatik (DE-588)4026894-9 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Textverstehendes System (DE-588)4284758-8 gnd rswk-swf Sprachverarbeitung (DE-588)4116579-2 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 s Automatische Sprachanalyse (DE-588)4129935-8 s Textverstehendes System (DE-588)4284758-8 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Sprachverarbeitung (DE-588)4116579-2 s Informatik (DE-588)4026894-9 s Howard, Cole Verfasser (DE-588)1190917912 aut Hapke, Hannes Max Verfasser (DE-588)1190917882 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030111475&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lane, Hobson Howard, Cole Hapke, Hannes Max Natural language processing in action understanding, analyzing, and generating text with Python Python Programmiersprache (DE-588)4434275-5 gnd Automatische Sprachanalyse (DE-588)4129935-8 gnd Informatik (DE-588)4026894-9 gnd Natürliche Sprache (DE-588)4041354-8 gnd Textverstehendes System (DE-588)4284758-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4129935-8 (DE-588)4026894-9 (DE-588)4041354-8 (DE-588)4284758-8 (DE-588)4116579-2 |
| title | Natural language processing in action understanding, analyzing, and generating text with Python |
| title_auth | Natural language processing in action understanding, analyzing, and generating text with Python |
| title_exact_search | Natural language processing in action understanding, analyzing, and generating text with Python |
| title_full | Natural language processing in action understanding, analyzing, and generating text with Python Hobson Lane, Cole Howard, Hannes Max Hapke |
| title_fullStr | Natural language processing in action understanding, analyzing, and generating text with Python Hobson Lane, Cole Howard, Hannes Max Hapke |
| title_full_unstemmed | Natural language processing in action understanding, analyzing, and generating text with Python Hobson Lane, Cole Howard, Hannes Max Hapke |
| title_short | Natural language processing in action |
| title_sort | natural language processing in action understanding analyzing and generating text with python |
| title_sub | understanding, analyzing, and generating text with Python |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Automatische Sprachanalyse (DE-588)4129935-8 gnd Informatik (DE-588)4026894-9 gnd Natürliche Sprache (DE-588)4041354-8 gnd Textverstehendes System (DE-588)4284758-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd |
| topic_facet | Python Programmiersprache Automatische Sprachanalyse Informatik Natürliche Sprache Textverstehendes System Sprachverarbeitung |
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