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Natural Language Processing and Computational Linguistics : a Practical Guide to Text Analysis with Python, Gensim, SpaCy, and Keras.
Discover how you can perform your own modern text analysis, to make predictions, create inferences, and gain insights about the data around you today. Learn how to harness the powerful Python ecosystem and tools such as spaCy and Gensim to perform natural language processing, and computational lingu...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Srinivasa-Desikan, Bhargav. | |
| 245 | 1 | 0 | |a Natural Language Processing and Computational Linguistics : |b a Practical Guide to Text Analysis with Python, Gensim, SpaCy, and Keras. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (298 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; 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. | |
| 505 | 8 | |a Named entity recognitionRule-based matching; Preprocessing; Summary; References; Chapter 4: Gensim -- Vectorizing Text and Transformations and n-grams; Introducing Gensim; Vectors and why we need them; Bag-of-words; TF-IDF; Other representations; Vector transformations in Gensim; 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? | |
| 505 | 8 | |a NER-tagging in PythonNER-tagging with spaCy; Training our own NER-taggers; NER-tagging examples and visualization; Summary; References; 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; Topic models in scikit-learn; Summary; References; Chapter 9: Advanced Topic Modeling; Advanced training tips. | |
| 505 | 8 | |a Exploring documentsTopic coherence and evaluating topic models; 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. | |
| 505 | 8 | |a Chapter 13: Deep Learning for TextDeep 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; References; Chapter 15: Sentiment Analysis and ChatBots; Sentiment analysis; Reddit for mining data; Twitter for mining data; ChatBots; Summary; References; Other Books You May Enjoy; Index. | |
| 520 | |a Discover how you can perform your own modern text analysis, to make predictions, create inferences, and gain insights about the data around you today. Learn how to harness the powerful Python ecosystem and tools such as spaCy and Gensim to perform natural language processing, and computational linguistics algorithms. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Natural language processing (Computer science) | |
| 650 | 0 | |a Computational linguistics. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 2 | |a Natural Language Processing | |
| 650 | 2 | |a Machine Learning | |
| 650 | 6 | |a Traitement automatique des langues naturelles. | |
| 650 | 6 | |a Linguistique informatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a computational linguistics. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Natural language & machine translation. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Natural Language Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Natural language processing (Computer science) |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 650 | 7 | |a Computational linguistics |2 fast | |
| 758 | |i has work: |a Natural language processing and computational linguistics (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGmjrwmmGvRybD4wxXkX7d |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Srinivasa-Desikan, Bhargav. |t Natural Language Processing and Computational Linguistics : A Practical Guide to Text Analysis with Python, Gensim, SpaCy, and Keras. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781788838535 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5446034 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n BDZ0037023390 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5446034 | ||
| 994 | |a 92 |b IZTAP | ||