- Table View
- List View
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 Elisabeth Métais Farid Meziane Helmut Horacek Philipp CimianoThis 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 Elisabeth Métais Farid Meziane 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
by Elisabeth Métais Farid Meziane Mohamad Saraee Vijayan Sugumaran Sunil VaderaThis book constitutes the refereed proceedings of the 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, held in Salford, UK, in June 2016. The 17 full papers, 22 short papers, and 13 poster papers presented were carefully reviewed and selected from 83 submissions. The papers cover the following topics: theoretical aspects, algorithms, applications, architectures for applied and integrated NLP, resources for applied NLP, and other aspects of NLP.
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 Elisabeth Métais Farid Meziane 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: 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 Elisabeth Métais Farid Meziane Sunil Vadera Vijayan Sugumaran Mohamad SaraeeThis 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: 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 Amon Rapp Luigi Di Caro Farid Meziane Vijayan SugumaranThe 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: 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 Paolo Rosso Valerio Basile Raquel Martínez Elisabeth Métais Farid MezianeThis 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: 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, Paris, France, June 13-15, 2018, Proceedings (Lecture Notes in Computer Science #10859)
by Max Silberztein Faten Atigui Elena Kornyshova Elisabeth Métais Farid MezianeThis book constitutes the refereed proceedings of the 23rd International Conference on Applications of Natural Language to Information Systems, NLDB 2018, held in Paris, France, in June 2018. The 18 full papers, 26 short papers, and 9 poster papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: Opinion Mining and Sentiment Analysis in Social Media; Semantics-Based Models and Applications; Neural Networks Based Approaches; Ontology Engineering; NLP; Text Similarities and Plagiarism Detection; Text Classification; Information Mining; Recommendation Systems; Translation and Foreign Language Querying; Software Requirement and Checking.
Natural Language Processing for Electronic Design Automation
by Mathias Soeken Rolf DrechslerThis 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 Rajesh Kumar Chakrawarti Ranjana Sikarwar Sanjaya Kumar Sarangi Samson Arun Raj Albert Raj Shweta Gupta Krishnan Sakthidasan Sankaran Romil RawatDiscover 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 Fundamentals: Build intelligent applications that can interpret the human language to deliver impactful results
by Dwight Gunning Sy Hwang Dongjun JungNatural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers, who want to gather and analyze text data to build an NLP-powered product. It'll help you to have prior experience of coding in Python - using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but is not necessary.
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 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 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 Healthcare: A Special Focus on Low Resource Languages (Innovations in Big Data and Machine Learning)
by Satya Ranjan Dash Shantipriya Parida Esaú Villatoro Tello Biswaranjan Acharya Ondřej BojarNatural Language Processing In Healthcare: A Special Focus on Low Resource Languages covers the theoretical and practical aspects as well as ethical and social implications of NLP in healthcare. It showcases the latest research and developments contributing to the rising awareness and importance of maintaining linguistic diversity. The book goes on to present current advances and scenarios based on solutions in healthcare and low resource languages and identifies the major challenges and opportunities that will impact NLP in clinical practice and health studies.
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 Projects: Build Next-Generation NLP Applications Using AI Techniques
by Akshay Kulkarni Adarsha Shivananda Anoosh KulkarniLeverage machine learning and deep learning techniques to build fully-fledged natural language processing (NLP) projects. Projects throughout this book grow in complexity and showcase methodologies, optimizing tips, and tricks to solve various business problems. You will use modern Python libraries and algorithms to build end-to-end NLP projects. The book starts with an overview of natural language processing (NLP) and artificial intelligence to provide a quick refresher on algorithms. Next, it covers end-to-end NLP projects beginning with traditional algorithms and projects such as customer review sentiment and emotion detection, topic modeling, and document clustering. From there, it delves into e-commerce related projects such as product categorization using the description of the product, a search engine to retrieve the relevant content, and a content-based recommendation system to enhance user experience. Moving forward, it explains how to build systems to find similar sentences using contextual embedding, summarizing huge documents using recurrent neural networks (RNN), automatic word suggestion using long short-term memory networks (LSTM), and how to build a chatbot using transfer learning. It concludes with an exploration of next-generation AI and algorithms in the research space. By the end of this book, you will have the knowledge needed to solve various business problems using NLP techniques.What You Will LearnImplement full-fledged intelligent NLP applications with PythonTranslate real-world business problem on text data with NLP techniquesLeverage machine learning and deep learning techniques to perform smart language processingGain hands-on experience implementing end-to-end search engine information retrieval, text summarization, chatbots, text generation, document clustering and product classification, and more Who This Book Is ForData scientists, machine learning engineers, and deep learning professionals looking to build natural language applications using Python
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python
by Akshay Kulkarni Adarsha ShivanandaImplement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity extraction, and sentiment analysis. Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You’ll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing.By using the recipes in this book, you will have a toolbox of solutions to apply to your own projects in the real world, making your development time quicker and more efficient. What You Will LearnApply NLP techniques using Python libraries such as NLTK, TextBlob, spaCy, Stanford CoreNLP, and many moreImplement the concepts of information retrieval, text summarization, sentiment analysis, and other advanced natural language processing techniques.Identify machine learning and deep learning techniques for natural language processing and natural language generation problemsWho This Book Is ForData scientists who want to refresh and learn various concepts of natural language processing through coding exercises.
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning Using Python
by Akshay Kulkarni Adarsha ShivanandaFocus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP. The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP. This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks. After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world.What You Will LearnKnow the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and moreImplement text pre-processing and feature engineering in NLP, including advanced methods of feature engineeringUnderstand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learningWho This Book Is ForData scientists who want to refresh and learn various concepts of natural language processing (NLP) through coding exercises
Natural Language Processing with AWS AI Services: Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend
by Mona M Premkumar Rangarajan Julien SimonWork 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: 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.