Semi Supervised Learning And Domain Adaptation In Natural Language Processing
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Semi Supervised Learning And Domain Adaptation In Natural Language Processing
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Author : Anders Søgaard
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Semi Supervised Learning And Domain Adaptation In Natural Language Processing written by Anders Søgaard and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees ("this algorithm never does too badly") than about useful rules of thumb ("in this case this algorithm may perform really well"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant.
Explainable Natural Language Processing
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Author : Anders Søgaard
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Explainable Natural Language Processing written by Anders Søgaard and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
This book presents a taxonomy framework and survey of methods relevant to explaining the decisions and analyzing the inner workings of Natural Language Processing (NLP) models. The book is intended to provide a snapshot of Explainable NLP, though the field continues to rapidly grow. The book is intended to be both readable by first-year M.Sc. students and interesting to an expert audience. The book opens by motivating a focus on providing a consistent taxonomy, pointing out inconsistencies and redundancies in previous taxonomies. It goes on to present (i) a taxonomy or framework for thinking about how approaches to explainable NLP relate to one another; (ii) brief surveys of each of the classes in the taxonomy, with a focus on methods that are relevant for NLP; and (iii) a discussion of the inherent limitations of some classes of methods, as well as how to best evaluate them. Finally, the book closes by providing a list of resources for further research on explainability.
Generalized Domain Adaptation For Sequence Labeling In Natural Language Processing
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Author : Min Xiao
language : en
Publisher:
Release Date : 2016
Generalized Domain Adaptation For Sequence Labeling In Natural Language Processing written by Min Xiao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Sequence labeling tasks have been widely studied in the natural language processing area, such as part-of-speech tagging, syntactic chunking, dependency parsing, and etc. Most of those systems are developed on a large amount of labeled training data via supervised learning. However, manually collecting labeled training data is too time-consuming and expensive. As an alternative, to alleviate the issue of label scarcity, domain adaptation has recently been proposed to train a statistical machine learning model in a target domain where there is no enough labeled training data by exploiting existing free labeled training data in a different but related source domain. The natural language processing community has witnessed the success of domain adaptation in a variety of sequence labeling tasks. Though the labeled training data in the source domain are available and free, however, they are not exactly as and can be very different from the test data in the target domain. Thus, simply applying naive supervised machine learning algorithms without considering domain differences may not fulfill the purpose. In this dissertation, we developed several novel representation learning approaches to address domain adaptation for sequence labeling in natural language processing. Those representation learning techniques aim to induce latent generalizable features to bridge domain divergence to enable cross-domain prediction. We first tackle a semi-supervised domain adaptation scenario where the target domain has a small amount of labeled training data and propose a distributed representation learning approach based on a probabilistic neural language model. We then relax the assumption of the availability of labeled training data in the target domain and study an unsupervised domain adaptation scenario where the target domain has only unlabeled training data, and give a task-informative representation learning approach based on dynamic dependency networks. Both works are developed in the setting where different domains contain sentences in different genres. We then extend and generalize domain adaptation into a more challenging scenario where different domains contain sentences in different languages and propose two cross-lingual representation learning approaches, one is based on deep neural networks with auxiliary bilingual word pairs and the other is based on annotation projection with auxiliary parallel sentences. All four specific learning scenarios are extensively evaluated with different sequence labeling tasks. The empirical results demonstrate the effectiveness of those generalized domain adaptation techniques for sequence labeling in natural language processing.
Introduction To Transfer Learning
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Author : Jindong Wang
language : en
Publisher: Springer Nature
Release Date : 2023-03-30
Introduction To Transfer Learning written by Jindong Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-30 with Computers categories.
Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.
Semisupervised Learning For Computational Linguistics
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Author : Steven Abney
language : en
Publisher: CRC Press
Release Date : 2007-09-17
Semisupervised Learning For Computational Linguistics written by Steven Abney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-17 with Business & Economics categories.
The rapid advancement in the theoretical understanding of statistical and machine learning methods for semisupervised learning has made it difficult for nonspecialists to keep up to date in the field. Providing a broad, accessible treatment of the theory as well as linguistic applications, Semisupervised Learning for Computational Linguistics offer
Deep Learning For Nlp And Speech Recognition
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Author : Uday Kamath
language : en
Publisher: Springer
Release Date : 2019-06-10
Deep Learning For Nlp And Speech Recognition written by Uday Kamath and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-10 with Computers categories.
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
Deep Learning For Natural Language Processing
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Author : Karthiek Reddy Bokka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-06-11
Deep Learning For Natural Language Processing written by Karthiek Reddy Bokka 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 2019-06-11 with Computers categories.
Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key FeaturesGain insights into the basic building blocks of natural language processingLearn how to select the best deep neural network to solve your NLP problemsExplore convolutional and recurrent neural networks and long short-term memory networksBook Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learnUnderstand various pre-processing techniques for deep learning problemsBuild a vector representation of text using word2vec and GloVeCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLPBuild a machine translation model in KerasDevelop a text generation application using LSTMBuild a trigger word detection application using an attention modelWho this book is for If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.
Domain Adaptation In Computer Vision With Deep Learning
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Author : Hemanth Venkateswara
language : en
Publisher: Springer Nature
Release Date : 2020-08-18
Domain Adaptation In Computer Vision With Deep Learning written by Hemanth Venkateswara and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-18 with Computers categories.
This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.
Machine Learning And Deep Learning In Natural Language Processing
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Author : Anitha S. Pillai
language : en
Publisher: CRC Press
Release Date : 2023-10-18
Machine Learning And Deep Learning In Natural Language Processing written by Anitha S. Pillai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-18 with Computers categories.
Natural Language Processing (NLP) is a sub-field of Artificial Intelligence, linguistics, and computer science and is concerned with the generation, recognition, and understanding of human languages, both written and spoken. NLP systems examine the grammatical structure of sentences as well as the specific meanings of words, and then they utilize algorithms to extract meaning and produce results. Machine Learning and Deep Learning in Natural Language Processing aims at providing a review of current Neural Network techniques in the NLP field, in particular about Conversational Agents (chatbots), Text-to-Speech, management of non-literal content – like emotions, but also satirical expressions – and applications in the healthcare field. NLP has the potential to be a disruptive technology in various healthcare fields, but so far little attention has been devoted to that goal. This book aims at providing some examples of NLP techniques that can, for example, restore speech, detect Parkinson’s disease, or help psychotherapists. This book is intended for a wide audience. Beginners will find useful chapters providing a general introduction to NLP techniques, while experienced professionals will appreciate the chapters about advanced management of emotion, empathy, and non-literal content.
Deep Learning For Natural Language Processing
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Author : Stephan Raaijmakers
language : en
Publisher: Simon and Schuster
Release Date : 2022-11-29
Deep Learning For Natural Language Processing written by Stephan Raaijmakers and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-29 with Computers categories.
Humans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.