Book Image

Natural Language Processing and Computational Linguistics

By : Bhargav Srinivasa-Desikan
Book Image

Natural Language Processing and Computational Linguistics

By: Bhargav Srinivasa-Desikan

Overview of this book

Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data. This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy. You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning. This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.

Related Content you might be interested in

Current Title:

Small book image
Natural Language Processing and Computational Linguistics
Bhargav Srinivasa-Desikan
Jun, 2018 | 306 pages
Small book image
Mastering spaCy
Duygu Altınok
Jul, 2021 | 356 pages
Small book image
The Handbook of NLP with Gensim
Chris Kuo
Oct, 2023 | 310 pages
Small book image
Natural Language Processing with Python Quick Start Guide
Nirant Kasliwal
Nov, 2018 | 182 pages
Table of Contents (22 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
What is Text Analysis?
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
2
Python Tips for Text Analysis
Python Tips for Text Analysis
Why Python?
Text manipulation in Python
Summary
References
3
spaCy's Language Models
spaCy's Language Models
spaCy
Installation
Tokenizing text
Summary
References
4
Gensim – Vectorizing Text and Transformations and n-grams
Gensim – Vectorizing Text and Transformations and n-grams
Introducing Gensim
Vectors and why we need them
Vector transformations in Gensim
n-grams and some more preprocessing
Summary
References
5
POS-Tagging and Its Applications
POS-Tagging and Its Applications
What is POS-tagging?
POS-tagging in Python
Training our own POS-taggers
POS-tagging code examples
Summary
References
6
NER-Tagging and Its Applications
NER-Tagging and Its Applications
What is NER-tagging?
NER-tagging in Python
Training our own NER-taggers
NER-tagging examples and visualization
Summary
References
7
Dependency Parsing
Dependency Parsing
Dependency parsing
Dependency parsing in Python
Dependency parsing with spaCy
Training our dependency parsers
Summary
References
8
Topic Models
Topic Models
What are topic models?
Topic models in Gensim
Topic models in scikit-learn
Summary
References
9
Advanced Topic Modeling
Advanced Topic Modeling
Advanced training tips
Exploring documents
Topic coherence and evaluating topic models
Visualizing topic models
Summary
References
10
Clustering and Classifying Text
Clustering and Classifying Text
Clustering text
Starting clustering
K-means
Hierarchical clustering
Classifying text
Summary
References
11
Similarity Queries and Summarization
Similarity Queries and Summarization
Similarity metrics
Similarity queries
Summarizing text
Summary
References
12
Word2Vec, Doc2Vec, and Gensim
Word2Vec, Doc2Vec, and Gensim
Word2Vec
Doc2Vec
Other word embeddings
Summary
References
13
Deep Learning for Text
Deep Learning for Text
Deep learning
Deep learning for text (and more)
Generating text
Summary
References
14
Keras and spaCy for Deep Learning
Keras and spaCy for Deep Learning
Keras and spaCy
Classification with Keras
Classification with spaCy
Summary
References
15
Sentiment Analysis and ChatBots
Sentiment Analysis and ChatBots
Sentiment analysis
ChatBots
Summary
References
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Index
Index

Training our dependency parsers

Again, if you have read Chapter 4, Gensim - Vectorizing Text and Transformations and n-grams, Chapter 5, POS-Tagging and Its applications, and Chapter 6, NER-Tagging and Its applications, then you would be comfortable with the theory behind training our own models in spaCy. We would recommend that you go back and read Vector transformations in Gensim section from chapter 4 and Training our own POS-taggers section from chapter 5 to refresh your ideas on what exactly training means in context with machine learning and in particular, spaCy.

Again, the advantage with spaCy is that we don't need to care about the algorithm being used under the hood, or which features are the best to select for dependency parsing - this is usually the hardest part of machine learning research. We know that an optimal learning algorithm has been selected, and all...

Previous Section
End of Section 3
Next Section