Generalized Domain Adaptation For Sequence Labeling In Natural Language Processing
DOWNLOAD
Download Generalized Domain Adaptation For Sequence Labeling In Natural Language Processing PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Generalized Domain Adaptation For Sequence Labeling In Natural Language Processing book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Generalized Domain Adaptation For Sequence Labeling In Natural Language Processing
DOWNLOAD
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.
Semi Supervised Learning And Domain Adaptation In Natural Language Processing
DOWNLOAD
Author : Anders Søgaard
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2013-05-01
Semi Supervised Learning And Domain Adaptation In Natural Language Processing written by Anders Søgaard 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 2013-05-01 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.
Trends In Parsing Technology
DOWNLOAD
Author : Harry Bunt
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-10-06
Trends In Parsing Technology written by Harry Bunt 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 2010-10-06 with Language Arts & Disciplines categories.
Computer parsing technology, which breaks down complex linguistic structures into their constituent parts, is a key research area in the automatic processing of human language. This volume is a collection of contributions from leading researchers in the field of natural language processing technology, each of whom detail their recent work which includes new techniques as well as results. The book presents an overview of the state of the art in current research into parsing technologies, focusing on three important themes: dependency parsing, domain adaptation, and deep parsing. The technology, which has a variety of practical uses, is especially concerned with the methods, tools and software that can be used to parse automatically. Applications include extracting information from free text or speech, question answering, speech recognition and comprehension, recommender systems, machine translation, and automatic summarization. New developments in the area of parsing technology are thus widely applicable, and researchers and professionals from a number of fields will find the material here required reading. As well as the other four volumes on parsing technology in this series this book has a breadth of coverage that makes it suitable both as an overview of the field for graduate students, and as a reference for established researchers in computational linguistics, artificial intelligence, computer science, language engineering, information science, and cognitive science. It will also be of interest to designers, developers, and advanced users of natural language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.
Recent Advances In Natural Language Processing Iii
DOWNLOAD
Author : Nicolas Nicolov
language : en
Publisher: John Benjamins Publishing
Release Date : 2004-11-30
Recent Advances In Natural Language Processing Iii written by Nicolas Nicolov and has been published by John Benjamins Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-11-30 with Language Arts & Disciplines categories.
This volume brings together revised versions of a selection of papers presented at the 2003 International Conference on “Recent Advances in Natural Language Processing”. A wide range of topics is covered in the volume: semantics, dialogue, summarization, anaphora resolution, shallow parsing, morphology, part-of-speech tagging, named entity, question answering, word sense disambiguation, information extraction. Various ‘state-of-the-art’ techniques are explored: finite state processing, machine learning (support vector machines, maximum entropy, decision trees, memory-based learning, inductive logic programming, transformation-based learning, perceptions), latent semantic analysis, constraint programming. The papers address different languages (Arabic, English, German, Slavic languages) and use different linguistic frameworks (HPSG, LFG, constraint-based DCG). This book will be of interest to those who work in computational linguistics, corpus linguistics, human language technology, translation studies, cognitive science, psycholinguistics, artificial intelligence, and informatics.
Neural Network Methods In Natural Language Processing
DOWNLOAD
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.
Handbook Of Natural Language Processing
DOWNLOAD
Author : Robert Dale
language : en
Publisher: CRC Press
Release Date : 2000-07-25
Handbook Of Natural Language Processing written by Robert Dale and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-07-25 with Business & Economics categories.
This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.
Natural Language Processing And Information Systems
DOWNLOAD
Author : Elisabeth Métais
language : en
Publisher: Springer Nature
Release Date : 2023-06-13
Natural Language Processing And Information Systems written by Elisabeth Métais and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-13 with Computers categories.
This 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, 2023 The 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.
Domain Adaptation In Natural Language Processing
DOWNLOAD
Author : Marina Sedinkina
language : en
Publisher:
Release Date : 2021
Domain Adaptation In Natural Language Processing written by Marina Sedinkina and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Domain Adaptation In Natural Language Processing
DOWNLOAD
Author : Jing Jiang
language : en
Publisher: ProQuest
Release Date : 2008
Domain Adaptation In Natural Language Processing written by Jing Jiang and has been published by ProQuest this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Although we focus on domain adaptation in natural language processing in this thesis, most of the analysis of the problem and the proposed domain adaptation techniques are not restricted to natural language processing problems but can be generally applied to most classification tasks when the training and the test domains differ.
Natural Language Processing And Information Systems
DOWNLOAD
Author : Zoubida Kedad
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-21
Natural Language Processing And Information Systems written by Zoubida Kedad 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 2007-06-21 with Computers categories.
This book constitutes the refereed proceedings of the 12th International Conference on Applications of Natural Language to Information Systems, NLDB 2007, held in Paris, France in June 2007. It covers natural language for database query processing, email management, semantic annotation, text clustering, ontology engineering, natural language for information system design, information retrieval systems, and natural language processing techniques.