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Build advanced Natural Language Understanding Systems by acquiring data and selecting appropriate technology. Key Features • Master NLU concepts from basic text processing to advanced deep learning techniques • Explore practical NLU applications like chatbots, sentiment analysis, and language translation • Gain a deeper understanding of large language models like ChatGPT Book Description Natural language understanding (NLU) organizes and structures, language allowing computer systems to effectively process textual information for many different practical applications. Natural Language Understanding with Python will help you explore practical techniques that make use of NLU to build a wide variety of creative and useful applications. Complete with step-by-step explanations of essential concepts and practical examples, this book begins by teaching you about NLU and its applications. You'll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you'll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you'll also be introduced to practical issues such as acquiring data, evaluating systems, and deploying NLU applications, along with their solutions. This book is a comprehensive guide that will help you explore the full spectrum of essential NLU techniques and resources. By the end of this book, you will be familiar with the foundational concepts of NLU, deep learning, and large language models (LLMs). You will be well on your way to having the skills to independently apply NLU technology in your own academic and practical applications. What you will learn • The most important skill that readers will acquire is not just HOW to apply natural language techniques, but WHY to select particular techniques. • The book will also cover important practical considerations concerning acquiring real data and evaluating real system performance, not just performing textbook evaluations with pre-existing corpora • After reading this book and studying the code, readers will be equipped to build state of the art as well as practical natural language applications to solve real problems. • How to develop and fine-tune an NLP application • Maintaining NLP applications after deployment Who this book is for This book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must. Cover Title Page Copyright Dedication Contributors Table of Contents Preface Part 1: Getting Started with Natural Language Understanding Technology Chapter 1: Natural Language Understanding, Related Technologies, and Natural Language Applications Understanding the basics of natural language Global considerations – languages, encodings, and translations The relationship between conversational AI and NLP Exploring interactive applications – chatbots and voice assistants Generic voice assistants Enterprise assistants Translation Education Exploring non-interactive applications Classification Sentiment analysis Spam and phishing detection Fake news detection Document retrieval Analytics Information extraction Translation Summarization, authorship, correcting grammar, and other applications A summary of the types of applications A look ahead – Python for NLP Summary Chapter 2: Identifying Practical Natural Language Understanding Problems Identifying problems that are the appropriate level of difficulty for the technology Looking at difficult applications of NLU Looking at applications that don’t need NLP Training data Application data Taking development costs into account Taking maintenance costs into account A flowchart for deciding on NLU applications Summary Part 2:Developing and Testing Natural Language Understanding Systems Chapter 3: Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning Rule-based approaches Words and lexicons Part-of-speech tagging Grammar Parsing Semantic analysis Pragmatic analysis Pipelines Traditional machine learning approaches Representing documents Classification Deep learning approaches Pre-trained models Considerations for selecting technologies Summary Chapter 4: Selecting Libraries and Tools for Natural Language Understanding Technical requirements Installing Python Developing software – JupyterLab and GitHub JupyterLab GitHub Exploring the libraries Using NLTK Using spaCy Using Keras Learning about other NLP libraries Choosing among NLP libraries Learning about other packages useful for NLP Looking at an example Setting up JupyterLab Processing one sentence Looking at corpus properties Summary Chapter 5: Natural Language Data – Finding and Preparing Data Finding sources of data and annotating it Finding data for your own application Finding data for a research project Metadata Generally available corpora Ensuring privacy and observing ethical considerations Ensuring the privacy of training data Ensuring the privacy of runtime data Treating human subjects ethically Treating crowdworkers ethically Preprocessing data Removing non-text Regularizing text Spelling correction Application-specific types of preprocessing Substituting class labels for words and numbers Redaction Domain-specific stopwords Remove HTML markup Data imbalance Using text preprocessing pipelines Choosing among preprocessing techniques Summary Chapter 6: Exploring and Visualizing Data Why visualize? Text document dataset – Sentence Polarity Dataset Data exploration Frequency distributions Measuring the similarities among documents General considerations for developing visualizations Using information from visualization to make decisions about processing Summary Chapter 7: Selecting Approaches and Representing Data Selecting NLP approaches Fitting the approach to the task Starting with the data Considering computational efficiency Initial studies Representing language for NLP applications Symbolic representations Representing language numerically with vectors Understanding vectors for document representation Representing words with context-independent vectors Word2Vec Representing words with context-dependent vectors Summary Chapter 8: Rule-Based Techniques Rule-based techniques Why use rules? Exploring regular expressions Recognizing, parsing, and replacing strings with regular expressions General tips for using regular expressions Word-level analysis Lemmatization Ontologies Sentence-level analysis Syntactic analysis Semantic analysis and slot filling Summary Chapter 9: Machine Learning Part 1 – Statistical Machine Learning A quick overview of evaluation Representing documents with TF-IDF and classifying with Naïve Bayes Summary of TF-IDF Classifying texts with Naïve Bayes TF-IDF/Bayes classification example Classifying documents with Support Vector Machines (SVMs) Slot-filling with CRFs Representing slot-tagged data Summary Chapter 10: Machine Learning Part 2 – Neural Networks and Deep Learning Techniques Basics of NNs Example – MLP for classification Hyperparameters and tuning Moving beyond MLPs – RNNs Looking at another approach – CNNs Summary Chapter 11: Machine Learning Part 3 – Transformers and Large Language Models Technical requirements Overview of transformers and LLMs Introducing attention Applying attention in transformers Leveraging existing data – LLMs or pre-trained models BERT and its variants Using BERT – a classification example Installing the data Splitting the data into training, validation, and testing sets Loading the BERT model Defining the model for fine-tuning Defining the loss function and metrics Defining the optimizer and the number of epochs Compiling the model Training the model Plotting the training process Evaluating the model on the test data Saving the model for inference Cloud-based LLMs ChatGPT Applying GPT-3 Summary Chapter 12: Applying Unsupervised Learning Approaches What is unsupervised learning? Topic modeling using clustering techniques and label derivation Grouping semantically similar documents Applying BERTopic to 20 newsgroups After clustering and topic labeling Making the most of data with weak supervision Summary Chapter 13: How Well Does It Work? – Evaluation Why evaluate an NLU system? Evaluation paradigms Comparing system results on standard metrics Evaluating language output Leaving out part of a system – ablation Shared tasks Data partitioning Evaluation metrics Accuracy and error rate Precision, recall, and F1 The receiver operating characteristic and area under the curve Confusion matrix User testing Statistical significance of differences Comparing three text classification methods A small transformer system TF-IDF evaluation A larger BERT model Summary Part 3: Systems in Action – Applying Natural Language Understanding at Scale Chapter 14: What to Do If the System Isn’t Working Technical requirements Figuring out that a system isn’t working Initial development Fixing accuracy problems Changing data Restructuring an application Moving on to deployment Problems after deployment Summary Chapter 15: Summary and Looking to the Future Overview of the book Potential for improvement – better accuracy and faster training Better accuracy Faster training Other areas for improvement Applications that are beyond the current state of the art Processing very long documents Understanding and creating videos Interpreting and generating sign languages Writing compelling fiction Future directions in NLU technology and research Quickly extending NLU technologies to new languages Real-time speech-to-speech translation Multimodal interaction Detecting and correcting bias Summary Further reading Index About Packt Other Books You May Enjoy Unleash the full potential of natural language understanding (NLU) and create impeccable systems by mastering the art of data acquisition and technology selection Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesMaster NLU concepts from basic text processing to advanced deep learning techniquesExplore practical NLU applications like chatbots, sentiment analysis, and language translationGain a deeper understanding of large language models like ChatGPTBook DescriptionNatural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications. Complete with step-by-step explanations of essential concepts and practical examples, you'll begin by learning about NLU and its applications. You'll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you'll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you'll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that'll help you explore techniques and resources that can be used for different applications in the future. By the end of this book, you'll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.What you will learnExplore the uses and applications of different NLP techniquesUnderstand practical data acquisition and system evaluation workflowsBuild cutting-edge and practical NLP applications to solve problemsMaster NLP development from selecting an application to deploymentOptimize NLP application maintenance after deploymentBuild a strong foundation in neural networks and deep learning for NLUWho this book is forThis book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must.