Book Image

Machine Learning Techniques for Text

By : Nikos Tsourakis
Book Image

Machine Learning Techniques for Text

By: Nikos Tsourakis

Overview of this book

With the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code. A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions. By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
1
Chapter 1: Introducing Machine Learning for Text
Chapter 1: Introducing Machine Learning for Text
The language phenomenon
The data explosion
The era of AI
Relevant research fields
The machine learning paradigm
Taxonomy of machine learning techniques
Visualization of the data
Evaluation of the results
Summary
Free Chapter
2
Chapter 2: Detecting Spam Emails
Chapter 2: Detecting Spam Emails
Technical requirements
Understanding spam detection
Extracting word representations
Executing data preprocessing
Performing classification
Measuring classification performance
Summary
3
Chapter 3: Classifying Topics of Newsgroup Posts
Chapter 3: Classifying Topics of Newsgroup Posts
Technical requirements
Understanding topic classification
Performing exploratory data analysis
Executing dimensionality reduction
Introducing the k-nearest neighbors algorithm
Introducing the random forest algorithm
Extracting word embedding representation
Summary
4
Chapter 4: Extracting Sentiments from Product Reviews
Chapter 4: Extracting Sentiments from Product Reviews
Technical requirements
Understanding sentiment analysis
Performing exploratory data analysis
Introducing linear regression
Introducing logistic regression
Introducing deep neural networks
Summary
5
Chapter 5: Recommending Music Titles
Chapter 5: Recommending Music Titles
Technical requirements
Understanding recommender systems
Performing exploratory data analysis
Introducing content-based filtering
Introducing collaborative filtering
Summary
6
Chapter 6: Teaching Machines to Translate
Chapter 6: Teaching Machines to Translate
Technical requirements
Understanding machine translation
Introducing rule-based machine translation
Introducing example-based machine translation
Introducing statistical machine translation
Introducing sequence-to-sequence learning
Measuring translation performance
Summary
7
Chapter 7: Summarizing Wikipedia Articles
Chapter 7: Summarizing Wikipedia Articles
Technical requirements
Understanding text summarization
Introducing web scraping
Performing extractive summarization
Performing abstractive summarization
Measuring summarization performance
Summary
8
Chapter 8: Detecting Hateful and Offensive Language
Chapter 8: Detecting Hateful and Offensive Language
Technical requirements
Introducing social networks
Understanding BERT
Introducing boosting algorithms
Creating validation sets
Treating imbalanced datasets
Classifying with BERT
Understanding CNN
Summary
9
Chapter 9: Generating Text in Chatbots
Chapter 9: Generating Text in Chatbots
Technical requirements
Understanding text generation
Creating a retrieval-based chatbot
Understanding language modeling
Creating a generative chatbot
Summary
10
Chapter 10: Clustering Speech-to-Text Transcriptions
Chapter 10: Clustering Speech-to-Text Transcriptions
Technical requirements
Understanding text clustering
Preprocessing the data
Using speech-to-text
Introducing the K-means algorithm
Introducing DBSCAN
Introducing the hierarchical clustering algorithm
Introducing the LDA algorithm
Summary
11
Index
Index
Why subscribe?
12
Other Books You May Enjoy
Other Books You May Enjoy
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Introducing the random forest algorithm

The method discussed in this section is based on the concept of ensemble learning, where multiple models (in our case, classifiers) are generated and combined to solve a particular problem. You can think of ensemble learning as having diverse people who bring different perspectives to the table for a decision. Ultimately, you want to harness those different perspectives and ensure a joint decision is reached.

A real-world example should shed some light on this type of learning. Suppose that you visit a city for the first time. After an exhausting day, there is finally some free time for dinner. One possible strategy in front of many dining choices is to walk around the city to find a good restaurant, a bistro, or a takeaway. Wandering around, the aim is to make the best possible choice for dinner based on several criteria (as in features), such as the quality of service, the ambience, and menu prices. Essentially, your brain runs a classification...

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