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Index
Natural Language Processing and Computational Linguistics Title Page Copyright and Credits Natural Language Processing and Computational Linguistics Packt Upsell Why subscribe? PacktPub.com Contributors About the author About the reviewers Packt is searching for authors like you Table of Contents 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 Other Books You May Enjoy Leave a review - let other readers know what you think
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