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Natural Language Processing and Information Systems: 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, Salford, UK, June 26–28, 2019, Proceedings (Lecture Notes in Computer Science #11608)
by Farid Meziane Elisabeth Métais Mohamad Saraee Vijayan Sugumaran Sunil VaderaThis book constitutes the refereed proceedings of the 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, held in Salford, UK, in June 2019. The 21 full papers and 16 short papers were carefully reviewed and selected from 75 submissions. The papers are organized in the following topical sections: argumentation mining and applications; deep learning, neural languages and NLP; social media and web analytics; question answering; corpus analysis; semantic web, open linked data, and ontologies; natural language in conceptual modeling; natural language and ubiquitous computing; and big data and business intelligence.
Natural Language Processing and Information Systems: 25th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, Saarbrücken, Germany, June 24–26, 2020, Proceedings (Lecture Notes in Computer Science #12089)
by Philipp Cimiano Farid Meziane Elisabeth Métais Helmut HoracekThis book constitutes the refereed proceedings of the 25th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, held in Saarbrücken, Germany, in June 2020.* The 15 full papers and 10 short papers were carefully reviewed and selected from 68 submissions. The papers are organized in the following topical sections: semantic analysis; question answering and answer generation; classification; sentiment analysis; personality, affect and emotion; retrieval, conversational agents and multimodal analysis. *The conference was held virtually due to the COVID-19 pandemic.
Natural Language Processing and Information Systems: 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, Saarbrücken, Germany, June 23–25, 2021, Proceedings (Lecture Notes in Computer Science #12801)
by Farid Meziane Elisabeth Métais Helmut Horacek Epaminondas KapetaniosThis book constitutes the refereed proceedings of the 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, held online in July 2021. The 19 full papers and 14 short papers were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: role of learning; methodological approaches; semantic relations; classification; sentiment analysis; social media; linking documents; multimodality; applications.
Natural Language Processing and Information Systems: 27th International Conference on Applications of Natural Language to Information Systems, NLDB 2022, Valencia, Spain, June 15–17, 2022, Proceedings (Lecture Notes in Computer Science #13286)
by Farid Meziane Elisabeth Métais Paolo Rosso Valerio Basile Raquel MartínezThis book constitutes the refereed proceedings of the 27th International Conference on Applications of Natural Language to Information Systems, NLDB 2022, held in Valencia, Spain in June 2022. The 28 full papers and 20 short papers were carefully reviewed and selected from 106 submissions. The papers are organized in the following topical sections: Sentiment Analysis and Social Media; Text Classification; Applications; Argumentation; Information Extraction and Linking; User Profiling; Semantics; Language Resources and Evaluation.
Natural Language Processing and Information Systems: 28th International Conference on Applications of Natural Language to Information Systems, NLDB 2023, Derby, UK, June 21–23, 2023, Proceedings (Lecture Notes in Computer Science #13913)
by Farid Meziane Elisabeth Métais Vijayan Sugumaran Warren Manning Stephan Reiff-MarganiecThis book constitutes the refereed proceedings of the 28th International Conference on Applications of Natural Language to Information Systems, NLDB 2023, held in Derby, UK, in June 21–23, 2023The 31 full papers and 14 short papers included in this book were carefully reviewed and selected from 89 submissions. They focus on the developments of the application of natural language to databases and information systems in the wider meaning of the term.
Natural Language Processing and Information Systems: 29th International Conference on Applications of Natural Language to Information Systems, NLDB 2024, Turin, Italy, June 25–27, 2024, Proceedings, Part I (Lecture Notes in Computer Science #14762)
by Farid Meziane Vijayan Sugumaran Amon Rapp Luigi Di CaroThe two-volume proceedings set LNCS 14762 and 14763 constitutes the refereed proceedings of the 29th International Conference on Applications of Natural Language to Information Systems, NLDB 2024, held in Turin, Italy, in June 25–27, 2024. The 35 full papers, 26 short papers, 3 demo papers and 8 industry track papers included in these books were carefully reviewed and selected from 141 submissions. They focus on advancements and support studies related to languages previously underrepresented, such as Arabic, Romanian, Italian and Japanese languages.
Natural Language Processing and Information Systems: 29th International Conference on Applications of Natural Language to Information Systems, NLDB 2024, Turin, Italy, June 25–27, 2024, Proceedings, Part II (Lecture Notes in Computer Science #14763)
by Farid Meziane Vijayan Sugumaran Amon Rapp Luigi Di CaroThe two-volume proceedings set LNCS 14762 and 14763 constitutes the refereed proceedings of the 29th International Conference on Applications of Natural Language to Information Systems, NLDB 2024, held in Turin, Italy, in June 25–27, 2024. The 35 full papers, 26 short papers, 3 demo papers and 8 industry track papers included in these books were carefully reviewed and selected from 141 submissions. They focus on advancements and support studies related to languages previously underrepresented, such as Arabic, Romanian, Italian and Japanese languages.
Natural Language Processing for Electronic Design Automation
by Rolf Drechsler Mathias SoekenThis book describes approaches for integrating more automation to the early stages of EDA design flows. Readers will learn how natural language processing techniques can be utilized during early design stages, in order to automate the requirements engineering process and the translation of natural language specifications into formal descriptions. This book brings together leading experts to explain the state-of-the-art in natural language processing, enabling designers to integrate these techniques into algorithms, through existing frameworks.
Natural Language Processing for Software Engineering
by Romil Rawat Rajesh Kumar Chakrawarti Sanjaya Kumar Sarangi Shweta Gupta Krishnan Sakthidasan Sankaran Ranjana Sikarwar Samson Arun Raj Albert RajDiscover how Natural Language Processing for Software Engineering can transform your understanding of agile development, equipping you with essential tools and insights to enhance software quality and responsiveness in today’s rapidly changing technological landscape. Agile development enhances business responsiveness through continuous software delivery, emphasizing iterative methodologies that produce incremental, usable software. Working software is the main measure of progress, and ongoing customer collaboration is essential. Approaches like Scrum, eXtreme Programming (XP), and Crystal share these principles but differ in focus: Scrum reduces documentation, XP improves software quality and adaptability to changing requirements, and Crystal emphasizes people and interactions while retaining key artifacts. Modifying software systems designed with Object-Oriented Analysis and Design can be costly and time-consuming in rapidly changing environments requiring frequent updates. This book explores how natural language processing can enhance agile methodologies, particularly in requirements engineering. It introduces tools that help developers create, organize, and update documentation throughout the agile project process.
Natural Language Processing in Action, Second Edition (In Action)
by Hobson Lane Maria DyshelDevelop your NLP skills from scratch, with an open source toolbox of Python packages, Transformers, Hugging Face, vector databases, and your own Large Language Models.Natural Language Processing in Action, Second Edition has helped thousands of data scientists build machines that understand human language. In this new and revised edition, you&’ll discover state-of-the art Natural Language Processing (NLP) models like BERT and HuggingFace transformers, popular open-source frameworks for chatbots, and more. You&’ll create NLP tools that can detect fake news, filter spam, deliver exceptional search results and even build truthfulness and reasoning into Large Language Models (LLMs). In Natural Language Processing in Action, Second Edition you will learn how to: • Process, analyze, understand, and generate natural language text • Build production-quality NLP pipelines with spaCy • Build neural networks for NLP using Pytorch • BERT and GPT transformers for English composition, writing code, and even organizing your thoughts • Create chatbots and other conversational AI agents In this new and revised edition, you&’ll discover state-of-the art NLP models like BERT and HuggingFace transformers, popular open-source frameworks for chatbots, and more. Plus, you&’ll discover vital skills and techniques for optimizing LLMs including conversational design, and automating the &“trial and error&” of LLM interactions for effective and accurate results. About the technology From nearly human chatbots to ultra-personalized business reports to AI-generated email, news stories, and novels, natural language processing (NLP) has never been more powerful! Groundbreaking advances in deep learning have made high-quality open source models and powerful NLP tools like spaCy and PyTorch widely available and ready for production applications. This book is your entrance ticket—and backstage pass—into the next generation of natural language processing. About the book Natural Language Processing in Action, Second Edition introduces the foundational technologies and state-of-the-art tools you&’ll need to write and publish NLP applications. You learn how to create custom models for search, translation, writing assistants, and more, without relying on big commercial foundation models. This fully updated second edition includes coverage of BERT, Hugging Face transformers, fine-tuning large language models, and more. What's inside • NLP pipelines with spaCy • Neural networks with PyTorch • BERT and GPT transformers • Conversational design for chatbots About the reader For intermediate Python programmers familiar with deep learning basics. About the author Hobson Lane is a data scientist and machine learning engineer with over twenty years of experience building autonomous systems and NLP pipelines. Maria Dyshel is a social entrepreneur and artificial intelligence expert, and the CEO and cofounder of Tangible AI. Cole Howard and Hannes Max Hapke were co-authors of the first edition. Table fo Contents Part 1 1 Machines that read and write: A natural language processing overview 2 Tokens of thought: Natural language words 3 Math with words: Term frequency–inverse document frequency vectors 4 Finding meaning in word counts: Semantic analysis Part 2 5 Word brain: Neural networks 6 Reasoning with word embeddings 7 Finding kernels of knowledge in text with CNNs 8 Reduce, reuse, and recycle your words: RNNs and LSTMs Part 3 9 Stackable deep learning: Transformers 10 Large language models in the real world 11 Information extraction and knowledge graphs 12 Getting chatty with dialog engines A Your NLP tools B Playful Python and regular e
Natural Language Processing in Action: Understanding, analyzing, and generating text with Python
by Hannes Hapke Cole Howard Hobson LaneSummaryNatural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyRecent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.About the BookNatural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.What's insideSome sentences in this book were written by NLP! Can you guess which ones?Working with Keras, TensorFlow, gensim, and scikit-learnRule-based and data-based NLPScalable pipelinesAbout the ReaderThis book requires a basic understanding of deep learning and intermediate Python skills.About the AuthorHobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production.Table of ContentsPART 1 - WORDY MACHINESPackets of thought (NLP overview)Build your vocabulary (word tokenization)Math with words (TF-IDF vectors)Finding meaning in word counts (semantic analysis)PART 2 - DEEPER LEARNING (NEURAL NETWORKS)Baby steps with neural networks (perceptrons and backpropagation)Reasoning with word vectors (Word2vec)Getting words in order with convolutional neural networks (CNNs)Loopy (recurrent) neural networks (RNNs)Improving retention with long short-term memory networksSequence-to-sequence models and attentionPART 3 - GETTING REAL (REAL-WORLD NLP CHALLENGES)Information extraction (named entity extraction and question answering)Getting chatty (dialog engines)Scaling up (optimization, parallelization, and batch processing)
Natural Language Processing in Artificial Intelligence—NLPinAI 2020 (Studies in Computational Intelligence #939)
by Roussanka LoukanovaThis book covers theoretical work, applications, approaches, and techniques for computational models of information and its presentation by language (artificial, human, or natural in other ways). Computational and technological developments that incorporate natural language are proliferating. Adequate coverage encounters difficult problems related to ambiguities and dependency on context and agents (humans or computational systems). The goal is to promote computational systems of intelligent natural language processing and related models of computation, language, thought, mental states, reasoning, and other cognitive processes.
Natural Language Processing in the Real World: Text Processing, Analytics, and Classification (Chapman & Hall/CRC Data Science Series)
by Jyotika SinghNatural Language Processing in the Real World is a practical guide for applying data science and machine learning to build Natural Language Processing (NLP) solutions. Where traditional, academic-taught NLP is often accompanied by a data source or dataset to aid solution building, this book is situated in the real world where there may not be an existing rich dataset. This book covers the basic concepts behind NLP and text processing and discusses the applications across 15 industry verticals. From data sources and extraction to transformation and modelling, and classic Machine Learning to Deep Learning and Transformers, several popular applications of NLP are discussed and implemented. This book provides a hands-on and holistic guide for anyone looking to build NLP solutions, from students of Computer Science to those involved in large-scale industrial projects.
Natural Language Processing on Oracle Cloud Infrastructure: Building Transformer-Based NLP Solutions Using Oracle AI and Hugging Face
by Hicham AssoudiThis book demonstrates how to use Oracle Cloud Infrastructure (OCI) and Hugging Face technologies to develop advanced NLP solutions. Through a practical case study, it addresses common NLP challenges and offers strategies for creating efficient, cost-effective transformer-based models. By the end of this book, you will have the skills and knowledge to create cutting-edge NLP solutions on OCI, customized to meet the needs of various industries and projects. The book takes you through the complete NLP solution life cycle—covering data preparation, model fine-tuning, deployment, and monitoring—while highlighting key topics such as cost-effectiveness and responsible AI for NLP implementations. Drawing from real-world experience and offering practical insights, it bridges the gap between theory and practice, equipping you to design and deploy scalable, cost-efficient NLP solutions. What You Will Learn Master key NLP concepts and the OCI ecosystem Create high-quality datasets using Hugging Face and OCI Data Labeling Service Fine-tune domain-specific pre-trained models from Hugging Face using OCI Data Science Notebook Sessions Deploy and operationalize your models with OCI Data Science Model Deployments Automate the NLP life cycle with OCI Data Science Pipelines Implement cost-effective strategies throughout the entire NLP life cycle, from dataset preparation to model training and deployment Who This Book Is For A diverse audience interested in implementing NLP solutions on Oracle Cloud Infrastructure: NLP practitioners, data scientists, and machine learning engineers who want to learn how to leverage Oracle AI and Hugging Face to implement an end-to-end NLP solution life cycle, from data preparation to model deployment; Oracle practitioners who want to expand their Oracle expertise by exploring OCI's advanced capabilities for building and scaling cutting-edge NLP solutions in enterprise environments; business decision makers who want to discover the strategic benefits of NLP solutions on OCI, including cost-effectiveness and responsible AI, while driving business value
Natural Language Processing with AWS AI Services: Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend
by Julien Simon Mona M Premkumar RangarajanWork through interesting real-life business use cases to uncover valuable insights from unstructured text using AWS AI servicesKey FeaturesGet to grips with AWS AI services for NLP and find out how to use them to gain strategic insightsRun Python code to use Amazon Textract and Amazon Comprehend to accelerate business outcomesUnderstand how you can integrate human-in-the-loop for custom NLP use cases with Amazon A2IBook DescriptionNatural language processing (NLP) uses machine learning to extract information from unstructured data. This book will help you to move quickly from business questions to high-performance models in production.To start with, you'll understand the importance of NLP in today's business applications and learn the features of Amazon Comprehend and Amazon Textract to build NLP models using Python and Jupyter Notebooks. The book then shows you how to integrate AI in applications for accelerating business outcomes with just a few lines of code. Throughout the book, you'll cover use cases such as smart text search, setting up compliance and controls when processing confidential documents, real-time text analytics, and much more to understand various NLP scenarios. You'll deploy and monitor scalable NLP models in production for real-time and batch requirements. As you advance, you'll explore strategies for including humans in the loop for different purposes in a document processing workflow. Moreover, you'll learn best practices for auto-scaling your NLP inference for enterprise traffic.Whether you're new to ML or an experienced practitioner, by the end of this NLP book, you'll have the confidence to use AWS AI services to build powerful NLP applications.What you will learnAutomate various NLP workflows on AWS to accelerate business outcomesUse Amazon Textract for text, tables, and handwriting recognition from images and PDF filesGain insights from unstructured text in the form of sentiment analysis, topic modeling, and more using Amazon ComprehendSet up end-to-end document processing pipelines to understand the role of humans in the loopDevelop NLP-based intelligent search solutions with just a few lines of codeCreate both real-time and batch document processing pipelines using PythonWho this book is forIf you're an NLP developer or data scientist looking to get started with AWS AI services to implement various NLP scenarios quickly, this book is for you. It will show you how easy it is to integrate AI in applications with just a few lines of code. A basic understanding of machine learning (ML) concepts is necessary to understand the concepts covered. Experience with Jupyter notebooks and Python will be helpful.
Natural Language Processing with Flair: A practical guide to understanding and solving NLP problems with Flair
by Tadej MagajnaLearn how to solve practical NLP problems with the Flair Python framework, train sequence labeling models, work with text classifiers and word embeddings, and much more through hands-on practical exercisesKey FeaturesBacked by the community and written by an NLP expertGet an understanding of basic NLP problems and terminologySolve real-world NLP problems with Flair with the help of practical hands-on exercisesBook DescriptionFlair is an easy-to-understand natural language processing (NLP) framework designed to facilitate training and distribution of state-of-the-art NLP models for named entity recognition, part-of-speech tagging, and text classification. Flair is also a text embedding library for combining different types of embeddings, such as document embeddings, Transformer embeddings, and the proposed Flair embeddings.Natural Language Processing with Flair takes a hands-on approach to explaining and solving real-world NLP problems. You'll begin by installing Flair and learning about the basic NLP concepts and terminology. You will explore Flair's extensive features, such as sequence tagging, text classification, and word embeddings, through practical exercises. As you advance, you will train your own sequence labeling and text classification models and learn how to use hyperparameter tuning in order to choose the right training parameters. You will learn about the idea behind one-shot and few-shot learning through a novel text classification technique TARS. Finally, you will solve several real-world NLP problems through hands-on exercises, as well as learn how to deploy Flair models to production.By the end of this Flair book, you'll have developed a thorough understanding of typical NLP problems and you'll be able to solve them with Flair.What you will learnGain an understanding of core NLP terminology and conceptsGet to grips with the capabilities of the Flair NLP frameworkFind out how to use Flair's state-of-the-art pre-built modelsBuild custom sequence labeling models, embeddings, and classifiersLearn about a novel text classification technique called TARSDiscover how to build applications with Flair and how to deploy them to productionWho this book is forThis Flair NLP book is for anyone who wants to learn about NLP through one of the most beginner-friendly, yet powerful Python NLP libraries out there. Software engineering students, developers, data scientists, and anyone who is transitioning into NLP and is interested in learning about practical approaches to solving problems with Flair will find this book useful. The book, however, is not recommended for readers aiming to get an in-depth theoretical understanding of the mathematics behind NLP. Beginner-level knowledge of Python programming is required to get the most out of this book.
Natural Language Processing with Java
by Richard M ReeseIf you are a Java programmer who wants to learn about the fundamental tasks underlying natural language processing, this book is for you. You will be able to identify and use NLP tasks for many common problems, and integrate them in your applications to solve more difficult problems. Readers should be familiar/experienced with Java software development.
Natural Language Processing with Java Cookbook: Over 70 recipes to create linguistic and language translation applications using Java libraries
by Richard M. ReeseA problem-solution guide to encounter various NLP tasks utilizing Java open source libraries and cloud-based solutionsKey FeaturesPerform simple-to-complex NLP text processing tasks using modern Java libraries Extract relationships between different text complexities using a problem-solution approach Utilize cloud-based APIs to perform machine translation operationsBook DescriptionNatural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon’s AWS. You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentences, or semantic words.What you will learnExplore how to use tokenizers in NLP processing Implement NLP techniques in machine learning and deep learning applications Identify sentences within the text and learn how to train specialized NER models Learn how to classify documents and perform sentiment analysis Find semantic similarities between text elements and extract text from a variety of sources Preprocess text from a variety of data sources Learn how to identify and translate languagesWho this book is forThis book is for data scientists, NLP engineers, and machine learning developers who want to perform their work on linguistic applications faster with the use of popular libraries on JVM machines. This book will help you build real-world NLP applications using a recipe-based approach. Prior knowledge of Natural Language Processing basics and Java programming is expected.
Natural Language Processing with Java and LingPipe Cookbook
by Krishna Dayanidhi Breck BaldwinThis book is for experienced Java developers with NLP needs, whether academics, industrialists, or hobbyists. A basic knowledge of NLP terminology will be beneficial.
Natural Language Processing with Java: Techniques for building machine learning and neural network models for NLP, 2nd Edition
by Richard M. Reese AshishSingh BhatiaExplore various approaches to organize and extract useful text from unstructured data using JavaKey FeaturesUse deep learning and NLP techniques in Java to discover hidden insights in textWork with popular Java libraries such as CoreNLP, OpenNLP, and MalletExplore machine translation, identifying parts of speech, and topic modelingBook DescriptionNatural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes.You’ll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you’ll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You’ll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You’ll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more.By the end of this book, you’ll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications.What you will learnUnderstand basic NLP tasks and how they relate to one anotherDiscover and use the available tokenization enginesApply search techniques to find people, as well as things, within a documentConstruct solutions to identify parts of speech within sentencesUse parsers to extract relationships between elements of a documentIdentify topics in a set of documentsExplore topic modeling from a documentWho this book is forNatural Language Processing with Java is for you if you are a data analyst, data scientist, or machine learning engineer who wants to extract information from a language using Java. Knowledge of Java programming is needed, while a basic understanding of statistics will be useful but not mandatory.
Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
by Delip Rao Brian McMahanNatural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations.Explore computational graphs and the supervised learning paradigmMaster the basics of the PyTorch optimized tensor manipulation libraryGet an overview of traditional NLP concepts and methodsLearn the basic ideas involved in building neural networksUse embeddings to represent words, sentences, documents, and other featuresExplore sequence prediction and generate sequence-to-sequence modelsLearn design patterns for building production NLP systems
Natural Language Processing with Python Cookbook
by Pratap Dangeti Krishna Bhavsar Naresh KumarLearn the tricks and tips that will help you design Text Analytics solutions About This Book • Independent recipes that will teach you how to efficiently perform Natural Language Processing in Python • Use dictionaries to create your own named entities using this easy-to-follow guide • Learn how to implement NLTK for various scenarios with the help of example-rich recipes to take you beyond basic Natural Language Processing Who This Book Is For This book is intended for data scientists, data analysts, and data science professionals who want to upgrade their existing skills to implement advanced text analytics using NLP. Some basic knowledge of Natural Language Processing is recommended. What You Will Learn • Explore corpus management using internal and external corpora • Learn WordNet usage and a couple of simple application assignments using WordNet • Operate on raw text • Learn to perform tokenization, stemming, lemmatization, and spelling corrections, stop words removals, and more • Understand regular expressions for pattern matching • Learn to use and write your own POS taggers and grammars • Learn to evaluate your own trained models • Explore Deep Learning techniques in NLP • Generate Text from Nietzsche's writing using LSTM • Utilize the BABI dataset and LSTM to model episodes In Detail Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora. This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques. By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python. Style and Approach This book's rich collection of recipes will come in handy when you are working with Natural Language Processing with Python. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf.
Natural Language Processing with Python Cookbook
by Krishna Bhavsar<P><P>Learn the tricks and tips that will help you design Text Analytics solutions <P><P>Key Features <P><P>Independent recipes that will teach you how to efficiently perform Natural Language Processing in Python <P><P>Use dictionaries to create your own named entities using this easy-to-follow guide <P><P>Learn how to implement NLTK for various scenarios with the help of example-rich recipes to take you beyond basic Natural Language Processing <P><P>Book Description <P><P>Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora. <P><P>This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques. <P><P>By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python. <P><P>What You Will Learn <P><P>Explore corpus management using internal and external corpora <P><P>Learn WordNet usage and a couple of simple application assignments using WordNet <P><P>Operate on raw text <P><P>Learn to perform tokenization, stemming, lemmatization, and spelling corrections, stop words removals, and more <P><P>Understand regular expressions for pattern matching <P><P>Learn to use and write your own POS taggers and grammars <P><P>Learn to evaluate your own trained models <P><P>Explore Deep Learning techniques in NLP <P><P>Generate Text from Nietzsche's writing using LSTM <P><P>Utilize the BABI dataset and LSTM to model episodes
Natural Language Processing with Python Quick Start Guide: Going from a Python developer to an effective Natural Language Processing Engineer
by Nirant KasliwalBuild and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learningKey FeaturesA no-math, code-driven programmer’s guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook DescriptionNLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP.The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications.We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn.We conclude by deploying these models as REST APIs with Flask.By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is forProgrammers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.
Natural Language Processing with Python and spaCy: A Practical Introduction
by Yuli VasilievAn introduction to natural language processing with Python using spaCy, a leading Python natural language processing library.Natural Language Processing with Python and spaCy will show you how to create NLP applications like chatbots, text-condensing scripts, and order-processing tools quickly and easily. You'll learn how to leverage the spaCy library to extract meaning from text intelligently; how to determine the relationships between words in a sentence (syntactic dependency parsing); identify nouns, verbs, and other parts of speech (part-of-speech tagging); and sort proper nouns into categories like people, organizations, and locations (named entity recognizing). You'll even learn how to transform statements into questions to keep a conversation going.You'll also learn how to: • Work with word vectors to mathematically find words with similar meanings (Chapter 5) • Identify patterns within data using spaCy's built-in displaCy visualizer (Chapter 7) • Automatically extract keywords from user input and store them in a relational database (Chapter 9) • Deploy a chatbot app to interact with users over the internet (Chapter 11)"Try This" sections in each chapter encourage you to practice what you've learned by expanding the book's example scripts to handle a wider range of inputs, add error handling, and build professional-quality applications.By the end of the book, you'll be creating your own NLP applications with Python and spaCy.