Machine learning techniques for text : apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation / Nikos Tsourakis.

Format:

Book

Author/Creator:

Tsourakis, Nikos, author.

Language:

English

Subjects (All):

Text data mining.

Machine learning.

Machine learning--Computer programs.

Python (Computer program language).

Physical Description:

1 online resource (448 pages)

Edition:

1st ed.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2022]

Summary:

Machine learning and Python offer unique opportunities to process text data. This book will equip you with the skills you need to undertake a role in the field. The content keeps the right balance between need-to-know theory and hands-on practice, grounding the discussion around different case studies.

Contents:

Cover

Title Page

Copyright and Credits

Acknowledgments

Contributors

Table of Contents

Preface

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

Supervised learning

Unsupervised learning

Semi-supervised learning

Reinforcement learning

Visualization of the data

Evaluation of the results

Summary

Chapter 2: Detecting Spam Emails

Technical requirements

Understanding spam detection

Explaining feature engineering

Extracting word representations

Using label encoding

Using one-hot encoding

Using token count encoding

Using tf-idf encoding

Executing data preprocessing

Tokenizing the input

Removing stop words

Stemming the words

Lemmatizing the words

Performing classification

Getting the data

Creating the train and test sets

Preprocessing the data

Extracting the features

Introducing the Support Vector Machines algorithm

Understanding Bayes' theorem

Measuring classification performance

Calculating accuracy

Calculating precision and recall

Calculating the F-score

Creating ROC and AUC

Creating precision-recall curves

Chapter 3: Classifying Topics of Newsgroup Posts

Understanding topic classification

Performing exploratory data analysis

Executing dimensionality reduction

Understanding principal component analysis

Understanding linear discriminant analysis

Putting PCA and LDA into action

Introducing the k-nearest neighbors algorithm

Performing feature extraction

Performing cross-validation

Comparison to the baseline model

Introducing the random forest algorithm.

Contracting a decision tree

Extracting word embedding representation

Understanding word embedding

Performing vector arithmetic

Using the fastText tool

Chapter 4: Extracting Sentiments from Product Reviews

Understanding sentiment analysis

Using the Software dataset

Exploiting the ratings of products

Extracting the word count of reviews

Exploiting the helpfulness score

Introducing linear regression

Putting linear regression into action

Introducing logistic regression

Understanding gradient descent

Using logistic regression

Creating training and test sets

Applying regularization

Introducing deep neural networks

Understanding logic gates

Understanding perceptrons

Understanding artificial neurons

Creating artificial neural networks

Training artificial neural networks

Chapter 5: Recommending Music Titles

Understanding recommender systems

Cleaning the data

Extracting information from the data

Understanding the Pearson correlation

Introducing content-based filtering

Extracting music recommendations

Introducing collaborative filtering

Using memory-based collaborative recommenders

Applying SVD

Clustering handwritten text

Applying t-SNE

Using model-based collaborative systems

Introducing autoencoders

Chapter 6: Teaching Machines to Translate

Understanding machine translation

Introducing rule-based machine translation

Using direct machine translation

Using transfer-based machine translation

Using interlingual machine translation.

Introducing example-based machine translation

Introducing statistical machine translation

Modeling the translation problem

Creating the models

Introducing sequence-to-sequence learning

Deciphering the encoder/decoder architecture

Understanding long short-term memory units

Putting seq2seq in action

Measuring translation performance

Chapter 7: Summarizing Wikipedia Articles

Understanding text summarization

Introducing web scraping

Scraping popular quotes

Scraping book reviews

Scraping Wikipedia articles

Performing extractive summarization

Performing abstractive summarization

Introducing the attention mechanism

Introducing transformers

Putting the transformer into action

Measuring summarization performance

Chapter 8: Detecting Hateful and Offensive Language

Introducing social networks

Understanding BERT

Pre-training phase

Fine-tuning phase

Putting BERT into action

Introducing boosting algorithms

Understanding AdaBoost

Understanding gradient boosting

Understanding XGBoost

Creating validation sets

Learning the myth of Icarus

Extracting the datasets

Treating imbalanced datasets

Classifying with BERT

Training the classifier

Applying early stopping

Understanding CNN

Adding pooling layers

Including CNN layers

Chapter 9: Generating Text in Chatbots

Understanding text generation

Creating a retrieval-based chatbot

Understanding language modeling

Understanding perplexity

Building a language model

Creating a generative chatbot

Using a pre-trained model

Creating the GUI

Creating the web chatbot

Fine-tuning a pre-trained model

Chapter 10: Clustering Speech-to-Text Transcriptions.

Technical requirements

Understanding text clustering

Using speech-to-text

Introducing the K-means algorithm

Putting K-means into action

Introducing DBSCAN

Putting DBSCAN into action

Assessing DBSCAN

Introducing the hierarchical clustering algorithm

Putting hierarchical clustering into action

Introducing the LDA algorithm

Putting LDA into action

Index

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Notes:

Includes index.

Description based on print version record.

ISBN:

9781803236292

1803236299

OCLC:

1350182782