Natural language processing and computational linguistics: a practical guide to text analysis with Python, Gensim, spaCy, and Keras
"This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algori...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK
Packt
[2018]
|
| Schriftenreihe: | Expert insight
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis"--Cover, page 4 |
| Beschreibung: | iv, 295 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9781788838535 178883853X |
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Datensatz im Suchindex
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|---|---|
| adam_text | Table of Contents Preface _____ ____________ 1 Chapter 1 : What is Text Analysis? What is text analysis? Where s the data at? Garbage in, garbage out Why should you do text analysis? Summary References Chapter 2: Python Tips for Text Analysis Why Python? Text manipulation in Python Summary References Chapter 3: spaCy s Language Models spaCy Installation Troubleshooting Language models Installing language models Installation - how and why? Basic preprocessing with language models Tokenizing text Part-of-speech (POS) - tagging Named entity recognition Rule-based matching Preprocessing Summary References Chapter 4: Gensim - Vectorizing Text and Transformations and ngrams Introducing Gensim Vectors and why we need them Bag-of-words TF-IDF Other representations Vector transformations in Gensim g g и 18 20 22 22 25 25 28 32 33 35 35 З8 38 зд 40 42 42 43 45 46 48 48 50 50 53 53 55 55 57 58 58
Table of Contents n-grams and some more preprocessing Summary References Chapter 5: POS-Tagging and Its Applications What is POS-tagging? POS-tagging in Python POS-tagging with spaCy Training our own POS-taggers POS-tagging code examples Summary References Chapter 6: NER-Tagging and Its Applications What is NER-tagging? NER-tagging in Python NER-tagging with spaCy 62 64 65 67 67 73 74 76 81 83 83 85 85 ցօ 93 Training our own NER-taggers NER-tagging examples and visualization Summary 104 106 References 106 Chapter 7: Dependency Parsing Dependency parsing Dependency parsing in Python Dependency parsing with spaCy Training our dependency parsers Summary References Chapter 8: Topic Models What are topic models? Topic models in Gensim Latent Dirichlet allocation Latent semantic indexing Hierarchical Dirichlet process Dynamic topic models 98 юэ 109 115 117 122 129 129 131 131 133 135 137 138 Topic models in scikit-learn Summary 141 141 145 References 145 Chapter 9: Advanced Topic Modeüng Advanced training tips Exploring documents Topic coherence and evaluating topic models 147 147 151 157
Table of Contents Visualizing topic models Summary References Chapter 10: Clustering and Classifying Text Clustering text Starting clustering K-means Hierarchical clustering Classifying text Summary References Chapter 11 : Similarity Queries and Summarization Similarity metrics Similarity queries Summarizing text Summary References Chapter 12: Word2Vec, Doc2Vec, and Gensim Word2Vec Using Word2Vec with Gensim Doc2Vec Other word embeddings GloVe FastText WordRank Varembed Poincare Summary References Chapter 13: Deep Learning for Text Deep learning Deep learning for text (and more) Generating text Summary References Chapter 14: Keras and spaCy for Deep Learning Keras and spaCy Classification with Keras Classification with spaCy Summary 160 165 166 16Ց 169 171 174 176 178 182 182 185 185 192 194 201 201 203 203 205 211 217 218 219 221 222 223 224 224 229 229 231 234 240 241 243 243 246 254 264 -------------------------------------------- iiii] -------------------------------------------
Table of Contents References Chapter 15: Sentiment Analysis and ChatBots Sentiment analysis Reddit for mining data Twitter for mining data ChatBots Summary References 264 267 267 271 273 275 285 285 Other Books You May Enjoy_____________________________________ 289 Index________________________________________________________ [iv] 293
|
| any_adam_object | 1 |
| author | Srinivasa-Desikan, Bhargav |
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| ctrlnum | (OCoLC)1099433602 (DE-599)BVBBV045529857 |
| discipline | Informatik |
| format | Book |
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| spelling | Srinivasa-Desikan, Bhargav Verfasser (DE-588)1186936894 aut Natural language processing and computational linguistics a practical guide to text analysis with Python, Gensim, spaCy, and Keras Bhargav Srinivasa-Desikan Birmingham, UK Packt [2018] © 2018 iv, 295 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Expert insight "This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis"--Cover, page 4 Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Automatische Sprachanalyse (DE-588)4129935-8 gnd rswk-swf Textverstehendes System (DE-588)4284758-8 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Natural language processing (Computer science) Computational linguistics Machine learning Python (Computer program language) 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 Erscheint auch als Online-Ausgabe 978-1-78883-703-3 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=030913999&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Srinivasa-Desikan, Bhargav Natural language processing and computational linguistics a practical guide to text analysis with Python, Gensim, spaCy, and Keras Natürliche Sprache (DE-588)4041354-8 gnd Automatische Sprachanalyse (DE-588)4129935-8 gnd Textverstehendes System (DE-588)4284758-8 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
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| title | Natural language processing and computational linguistics a practical guide to text analysis with Python, Gensim, spaCy, and Keras |
| title_auth | Natural language processing and computational linguistics a practical guide to text analysis with Python, Gensim, spaCy, and Keras |
| title_exact_search | Natural language processing and computational linguistics a practical guide to text analysis with Python, Gensim, spaCy, and Keras |
| title_full | Natural language processing and computational linguistics a practical guide to text analysis with Python, Gensim, spaCy, and Keras Bhargav Srinivasa-Desikan |
| title_fullStr | Natural language processing and computational linguistics a practical guide to text analysis with Python, Gensim, spaCy, and Keras Bhargav Srinivasa-Desikan |
| title_full_unstemmed | Natural language processing and computational linguistics a practical guide to text analysis with Python, Gensim, spaCy, and Keras Bhargav Srinivasa-Desikan |
| title_short | Natural language processing and computational linguistics |
| title_sort | natural language processing and computational linguistics a practical guide to text analysis with python gensim spacy and keras |
| title_sub | a practical guide to text analysis with Python, Gensim, spaCy, and Keras |
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