Name: Natural Language Processing and Computational Linguistics
Author: Bhargav Srinivasa Desikan
ISBN: 9781788837033

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Natural Language Processing and Computational Linguistics

Bhargav Srinivasa Desikan

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Copyright and Credits
Natural Language Processing and Computational Linguistics
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PacktPub.com
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
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
Python Tips for Text Analysis
Why Python?
Text manipulation in Python
Summary
References
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
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
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
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
Dependency Parsing
Dependency parsing
Dependency parsing in Python
Dependency parsing with spaCy
Training our dependency parsers
Summary
References
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
Advanced Topic Modeling
Advanced training tips
Exploring documents
Topic coherence and evaluating topic models
Visualizing topic models
Summary
References
Clustering and Classifying Text
Clustering text
Starting clustering
K-means
Hierarchical clustering
Classifying text
Summary
References
Similarity Queries and Summarization
Similarity metrics
Similarity queries
Summarizing text
Summary
References
Word2Vec, Doc2Vec, and Gensim
Word2Vec
Using Word2Vec with Gensim
Doc2Vec
Other word embeddings
GloVe
FastText
WordRank
Varembed
Poincare
Summary
References
Deep Learning for Text
Deep learning
Deep learning for text (and more)
Generating text
Summary
References
Keras and spaCy for Deep Learning
Keras and spaCy
Classification with Keras
Classification with spaCy
Summary
References
Sentiment Analysis and ChatBots
Sentiment analysis
Reddit for mining data
Twitter for mining data
ChatBots
Summary
References
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Natural Language Processing and Computational Linguistics