Semisupervised Learning For Computational Linguistics

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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

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.

Chinese Computational Linguistics And Natural Language Processing Based On Naturally Annotated Big Data

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Author : Maosong Sun
language : en
Publisher: Springer
Release Date : 2013-10-04

Chinese Computational Linguistics And Natural Language Processing Based On Naturally Annotated Big Data written by Maosong Sun and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-04 with Computers categories.

This book constitutes the refereed proceedings of the 12th China National Conference on Computational Linguistics, CCL 2013, and of the First International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2013, held in Suzhou, China, in October 2013. The 32 papers presented were carefully reviewed and selected from 252 submissions. The papers are organized in topical sections on word segmentation; open-domain question answering; discourse, coreference and pragmatics; statistical and machine learning methods in NLP; semantics; text mining, open-domain information extraction and machine reading of the Web; sentiment analysis, opinion mining and text classification; lexical semantics and ontologies; language resources and annotation; machine translation; speech recognition and synthesis; tagging and chunking; and large-scale knowledge acquisition and reasoning.

Natural Language Annotation For Machine Learning

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Author : James Pustejovsky
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013

Natural Language Annotation For Machine Learning written by James Pustejovsky and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computers categories.

Includes bibliographical references (p. 305-315) and index.

Introduction To Semi Supervised Learning

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Author : Xiaojin Geffner
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Introduction To Semi Supervised Learning written by Xiaojin Geffner 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.

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook

Acl 2007

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Author : Association for Computational Linguistics. Meeting
language : en
Publisher:
Release Date : 2007

Acl 2007 written by Association for Computational Linguistics. Meeting and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computational linguistics categories.

Neural Network Methods In Natural Language Processing

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Author : Yoav Goldberg
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2017-04-17

Neural Network Methods In Natural Language Processing written by Yoav Goldberg and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-17 with Computers categories.

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Semantic Domains In Computational Linguistics

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Author : Alfio Gliozzo
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-31

Semantic Domains In Computational Linguistics written by Alfio Gliozzo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-31 with Language Arts & Disciplines categories.

Semantic fields are lexically coherent – the words they contain co-occur in texts. In this book the authors introduce and define semantic domains, a computational model for lexical semantics inspired by the theory of semantic fields. Semantic domains allow us to exploit domain features for texts, terms and concepts, and they can significantly boost the performance of natural-language processing systems. Semantic domains can be derived from existing lexical resources or can be acquired from corpora in an unsupervised manner. They also have the property of interlinguality, and they can be used to relate terms in different languages in multilingual application scenarios. The authors give a comprehensive explanation of the computational model, with detailed chapters on semantic domains, domain models, and applications of the technique in text categorization, word sense disambiguation, and cross-language text categorization. This book is suitable for researchers and graduate students in computational linguistics.

Semi Supervised Learning

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Author : Olivier Chapelle
language : en
Publisher: MIT Press
Release Date : 2010-01-22

Semi Supervised Learning written by Olivier Chapelle and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-22 with Computers categories.

A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.