Natural Language Processing and Computational Linguistics电子书

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Natural Language Processing and Computational Linguistics

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Contributors

About the author

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Preface

Who this book is for

What this book covers

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Conventions used

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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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