Natural Language Processing With Flair
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Natural Language Processing With Flair
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Author : Tadej Magajna
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-29
Natural Language Processing With Flair written by Tadej Magajna and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-29 with Computers categories.
Learn 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 exercises Key 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 Description Flair 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 for This 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 Fundamentals For Developers
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Author : Oswald Campesato
language : en
Publisher: Mercury Learning and Information
Release Date : 2021-06-14
Natural Language Processing Fundamentals For Developers written by Oswald Campesato and has been published by Mercury Learning and Information this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-14 with Computers categories.
This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA (“state of the art”). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included. FEATURES: Covers extensive topics related to natural language processing Includes separate appendices on regular expressions and probability/statistics Features companion files with source code and figures from the book. The companion files are available online by emailing the publisher with proof of purchase at [email protected].
Getting Started With Deep Learning For Natural Language Processing
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Author : Sunil Patel
language : en
Publisher: BPB Publications
Release Date : 2021-01-13
Getting Started With Deep Learning For Natural Language Processing written by Sunil Patel and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-13 with Computers categories.
Learn how to redesign NLP applications from scratch. KEY FEATURESÊÊ ¥ Get familiar with the basics of any Machine Learning or Deep Learning application. ¥ Understand how does preprocessing work in NLP pipeline. ¥ Use simple PyTorch snippets to create basic building blocks of the network commonly used inÊ NLP.Ê ¥ Learn how to build a complex NLP application. ¥ Get familiar with the advanced embedding technique, Generative network, and Audio signal processing techniques. ÊÊ DESCRIPTIONÊ Natural language processing (NLP) is one of the areas where many Machine Learning and Deep Learning techniques are applied. This book covers wide areas, including the fundamentals of Machine Learning, Understanding and optimizing Hyperparameters, Convolution Neural Networks (CNN), and Recurrent Neural Networks (RNN). This book not only covers the classical concept of text processing but also shares the recent advancements. This book will empower users in designing networks with the least computational and time complexity. This book not only covers basics of Natural Language Processing but also helps in deciphering the logic behind advanced concepts/architecture such as Batch Normalization, Position Embedding, DenseNet, Attention Mechanism, Highway Networks, Transformer models and Siamese Networks. This book also covers recent advancements such as ELMo-BiLM, SkipThought, and Bert. This book also covers practical implementation with step by step explanation of deep learning techniques in Topic Modelling, Text Generation, Named Entity Recognition, Text Summarization, and Language Translation. In addition to this, very advanced and open to research topics such as Generative Adversarial Network and Speech Processing are also covered. WHAT YOU WILL LEARNÊ ¥ Learn how to leveraging GPU for Deep Learning ¥ Learn how to use complex embedding models such as BERT ¥ Get familiar with the common NLP applications. ¥ Learn how to use GANs in NLP ¥ Learn how to process Speech data and implementing it in Speech applications Ê WHO THIS BOOK IS FORÊ This book is a must-read to everyone who wishes to start the career with Machine learning and Deep Learning. This book is also for those who want to use GPU for developing Deep Learning applications. TABLE OF CONTENTSÊÊ 1. Understanding the basics of learning Process 2. Text Processing Techniques 3. Representing Language Mathematically 4. Using RNN for NLP 5. Applying CNN In NLP Tasks 6. Accelerating NLP with Advanced Embeddings 7. Applying Deep Learning to NLP tasks 8. Application of Complex Architectures in NLP 9. Understanding Generative Networks 10. Techniques of Speech Processing 11. The Road Ahead
Nlp For Sentiment Analysis
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Author : Prof. Dr. Dileep Kumar M.
language : en
Publisher: Xoffencer International Book Publication
Release Date : 2024-10-25
Nlp For Sentiment Analysis written by Prof. Dr. Dileep Kumar M. and has been published by Xoffencer International Book Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-25 with Computers categories.
Natural Language Processing (NLP) has become a cornerstone in extracting and interpreting human emotions and opinions from text data, and one of its significant applications is sentiment analysis. Sentiment analysis aims to automatically identify subjective information within text, often categorizing sentiments as positive, negative, or neutral. This ability to quantify opinion and emotion has garnered interest from a broad range of industries—marketing, healthcare, finance, and customer service, to name a few—as organizations increasingly rely on insights derived from unstructured data like social media posts, reviews, and feedback forms. The rise in data-driven decision-making further underscores the importance of sentiment analysis, positioning it as a valuable tool in understanding public opinion, customer satisfaction, and user experience. With NLP, sentiment analysis transforms complex linguistic expressions into structured, analyzable data, enabling businesses and researchers to gauge public mood and predict behavior, thus facilitating more responsive and personalized services. Sentiment analysis is inherently challenging, however, as it requires deep comprehension of language structure, context, and the subtleties of human expression. Human language is diverse and laden with intricacies, including sarcasm, humor, regional dialects, and idiomatic expressions, which can complicate straightforward sentiment categorization. Modern sentiment analysis leverages a combination of machine learning, deep learning, and lexicon-based approaches to overcome these obstacles. Machine learning models like Support Vector Machines, Naive Bayes, and increasingly complex neural networks have been employed to classify sentiment, often with notable success. Deep learning, particularly through techniques such as Long Short-Term Memory (LSTM) networks and Transformer-based architectures like BERT and GPT, has further advanced sentiment analysis by enabling models to process long text sequences and capture contextual nuances. Lexicon-based approaches, on the other hand, involve predefined lists of words associated with sentiment, offering a more rule-based approach that can be useful in specific applications or as a complement to machine learning methods. In recent years, transfer learning has brought about substantial improvements in NLP for sentiment analysis, particularly through pretrained models that allow for fine-tuning on sentiment-specific tasks with minimal labeled data.
Feature Store For Machine Learning
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Author : Jayanth Kumar M J
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-06-30
Feature Store For Machine Learning written by Jayanth Kumar M J and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-30 with Computers categories.
Learn how to leverage feature stores to make the most of your machine learning models Key Features • Understand the significance of feature stores in the ML life cycle • Discover how features can be shared, discovered, and re-used • Learn to make features available for online models during inference Book Description Feature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started. Feature Store for Machine Learning is for data scientists who want to learn how to use feature stores to share and reuse each other's work and expertise. You'll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model. While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time. By the end of this book, you'll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud. What you will learn • Understand the significance of feature stores in a machine learning pipeline • Become well-versed with how to curate, store, share and discover features using feature stores • Explore the different components and capabilities of a feature store • Discover how to use feature stores with batch and online models • Accelerate your model life cycle and reduce costs • Deploy your first feature store for production use cases Who this book is for If you have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them, then this book is for you. Data/machine learning engineers and data scientists who build machine learning models for production systems in any domain, those supporting data engineers in productionizing ML models, and platform engineers who build data science (ML) platforms for the organization will also find plenty of practical advice in the later chapters of this book.
Natural Language Processing In Artificial Intelligence Nlpinai 2020
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Author : Roussanka Loukanova
language : en
Publisher: Springer Nature
Release Date : 2021-03-25
Natural Language Processing In Artificial Intelligence Nlpinai 2020 written by Roussanka Loukanova and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-25 with Technology & Engineering categories.
This 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.
Handbook Of Investment Analysis Portfolio Management And Financial Derivatives In 4 Volumes
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Author : Cheng Few Lee
language : en
Publisher: World Scientific
Release Date : 2024-04-08
Handbook Of Investment Analysis Portfolio Management And Financial Derivatives In 4 Volumes written by Cheng Few Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-08 with Business & Economics categories.
This four-volume handbook covers important topics in the fields of investment analysis, portfolio management, and financial derivatives. Investment analysis papers cover technical analysis, fundamental analysis, contrarian analysis, and dynamic asset allocation. Portfolio analysis papers include optimization, minimization, and other methods which will be used to obtain the optimal weights of portfolio and their applications. Mutual fund and hedge fund papers are also included as one of the applications of portfolio analysis in this handbook.The topic of financial derivatives, which includes futures, options, swaps, and risk management, is very important for both academicians and partitioners. Papers of financial derivatives in this handbook include (i) valuation of future contracts and hedge ratio determination, (ii) options valuation, hedging, and their application in investment analysis and portfolio management, and (iii) theories and applications of risk management.Led by worldwide known Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues of investment analysis, portfolio management, and financial derivatives based on his years of academic and industry experience.
Language Intelligence
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Author : Akshi Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2025-01-15
Language Intelligence written by Akshi Kumar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-15 with Computers categories.
Thorough review of foundational concepts and advanced techniques in natural language processing (NLP) and its impact across sectors Supported by examples and case studies throughout, Language Intelligence provides an in-depth exploration of the latest advancements in natural language processing (NLP), offering a unique blend of insight on theoretical foundations, practical applications, and future directions in the field. Comprised of 10 chapters, this book provides a thorough understanding of both foundational concepts and advanced techniques, starting with an overview of the historical development of NLP and essential mechanisms of Natural Language Understanding (NLU) and Natural Language Generation (NLG). It delves into the data landscape crucial for NLP, emphasizing ethical considerations, and equips readers with fundamental text processing techniques. The book also discusses linguistic features central to NLP and explores computational and cognitive approaches that enrich the field’s advancement. Practical applications and advanced processing techniques across various sectors like healthcare, legal, finance, and education are showcased, along with a critical examination of NLP metrics and methods for evaluation. The appendices offer detailed explorations of text representation methods, advanced applications, and Python’s NLP capabilities, aiming to inform, inspire, and ignite a passion for NLP in the ever-expanding digital universe. Written by a highly qualified academic with significant research experience in the field, Language Intelligence covers topics including: Fundamental text processing, covering text cleaning, sentence splitting, tokenization, lemmatization and stemming, stop-word removal, part-of-speech tagging, and parsing and syntactic analysis Computational and cognitive approaches, covering human-like reasoning, transfer learning, and learning with minimal examples Affective, psychological, and content analysis, covering sentiment analysis, emotion recognition, irony, humour, and sarcasm detection, and indicators of distress Multilingual natural language processing, covering translation and transliteration, cross-lingual models and embeddings, low-resource language processing, and cultural nuance and idiom recognition Language Intelligence is an ideal reference for professionals across sectors and graduate students in related programs of study who have a foundational understanding of computer science, linguistics, and artificial intelligence looking to delve deeper into the intricacies of NLP.
Human Language Technology Challenges For Computer Science And Linguistics
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Author : Zygmunt Vetulani
language : en
Publisher: Springer Nature
Release Date : 2022-06-04
Human Language Technology Challenges For Computer Science And Linguistics written by Zygmunt Vetulani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-04 with Computers categories.
This book constitutes the refereed proceedings of the 9th Language and Technology Conference: Challenges for Computer Science and Linguistics, LTC 2019, held in Poznan, Poland, in May 2019. The 24 revised papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers are categorized into the following topical sub-headings: Speech Processing; Language Resources and Tools; Computational Semantics; Emotions, Decisions and Opinions; Digital Humanities; Evaluation; and Legal Aspects.
Advances Of Science And Technology
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Author : Mulatu Liyew Berihun
language : en
Publisher: Springer Nature
Release Date : 2022-01-01
Advances Of Science And Technology written by Mulatu Liyew Berihun and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-01 with Computers categories.
This two-volume set of LNICST 411 and 412 constitutes the refereed post-conference proceedings of the 9th International Conference on Advancement of Science and Technology, ICAST 2021, which took place in August 2021. Due to COVID-19 pandemic the conference was held virtually. The 80 revised full papers were carefully reviewed and selected from 202 submissions. The papers present economic and technologic developments in modern societies in 7 tracks: Chemical, Food and Bioprocess Engineering; Electrical and Electronics Engineering; ICT, Software and Hardware Engineering; Civil, Water Resources, and Environmental Engineering ICT; Mechanical and Industrial Engineering; Material Science and Engineering; Energy Science, Engineering and Policy.