Card | Table | RUSMARC | |
Srinivasa-Desikan, Bhargav. Natural language processing and computational linguistics: a practical guide to text analysis with Python, Gensim, spaCy, and Keras / Bhargav Srinivasa-Desikan. — 1 online resource (1 volume) : illustrations — <URL:http://elib.fa.ru/ebsco/1841858.pdf>.Record create date: 7/31/2018 Subject: Natural language processing (Computer science); Computational linguistics.; Machine learning.; Python (Computer program language); Computational linguistics.; Machine learning.; Natural language processing (Computer science); Python (Computer program language); COMPUTERS / General Collections: EBSCO Allowed Actions: –
Action 'Read' will be available if you login or access site from another network
Action 'Download' will be available if you login or access site from another network
Group: Anonymous Network: Internet |
Document access rights
Network | User group | Action | ||||
---|---|---|---|---|---|---|
Finuniversity Local Network | All | |||||
Internet | Readers | |||||
Internet | Anonymous |
Table of Contents
- 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
- Named entity recognition
- Rule-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?
- NER-tagging in Python
- NER-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
- Exploring documents
- Topic 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
- Word2Vec
- 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
- References
- Chapter 15: Sentiment Analysis and ChatBots
- Sentiment analysis
- Reddit for mining data
- Twitter for mining data
- ChatBots
- Summary
- References
- Sentiment analysis
- Other Books You May Enjoy
- Index
Usage statistics
Access count: 0
Last 30 days: 0 Detailed usage statistics |