Semi-Supervised Dependency Parsing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Semi-Supervised Dependency Parsing write by Wenliang Chen. This book was released on 2015-07-16. Semi-Supervised Dependency Parsing available in PDF, EPUB and Kindle. This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with relatively small labeled data and have shown their advantages in the context of dependency parsing for many languages. Various semi-supervised dependency parsing approaches have been proposed in recent works which utilize different types of information gleaned from unlabeled data. The book offers readers a comprehensive introduction to these approaches, making it ideally suited as a textbook for advanced undergraduate and graduate students and researchers in the fields of syntactic parsing and natural language processing.
Semi-supervised Methods for Out-of-domain Dependency Parsing
Semi-supervised Methods for Out-of-domain Dependency Parsing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Semi-supervised Methods for Out-of-domain Dependency Parsing write by Juntao Yu. This book was released on 2018. Semi-supervised Methods for Out-of-domain Dependency Parsing available in PDF, EPUB and Kindle.
Semi-Supervised Learning and Domain Adaptation in Natural Language Processing
Semi-Supervised Learning and Domain Adaptation in Natural Language Processing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Semi-Supervised Learning and Domain Adaptation in Natural Language Processing write by Anders Søgaard. This book was released on 2022-05-31. Semi-Supervised Learning and Domain Adaptation in Natural Language Processing available in PDF, EPUB and Kindle. 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.
Dependency Parsing
Dependency Parsing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dependency Parsing write by Sandra Kübler. This book was released on 2009. Dependency Parsing available in PDF, EPUB and Kindle. Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts
Advances in Discriminative Dependency Parsing
Advances in Discriminative Dependency Parsing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances in Discriminative Dependency Parsing write by Terry Y. Koo. This book was released on 2010. Advances in Discriminative Dependency Parsing available in PDF, EPUB and Kindle. Achieving a greater understanding of natural language syntax and parsing is a critical step in producing useful natural language processing systems. In this thesis, we focus on the formalism of dependency grammar as it allows one to model important head modifier relationships with a minimum of extraneous structure. Recent research in dependency parsing has highlighted the discriminative structured prediction framework (McDonald et al., 2005a; Carreras, 2007; Suzuki et al., 2009), which is characterized by two advantages: first, the availability of powerful discriminative learning algorithms like log-linear and max-margin models (Lafferty et al., 2001; Taskar et al., 2003), and second, the ability to use arbitrarily-defined feature representations. This thesis explores three advances in the field of discriminative dependency parsing. First, we show that the classic Matrix-Tree Theorem (Kirchhoff, 1847; Tutte, 1984) can be applied to the problem of non-projective dependency parsing, enabling both log-linear and max-margin parameter estimation in this setting. Second, we present novel third-order dependency parsing algorithms that extend the amount of context available to discriminative parsers while retaining computational complexity equivalent to existing second-order parsers. Finally, we describe a simple but effective method for augmenting the features of a dependency parser with information derived from standard clustering algorithms; our semi-supervised approach is able to deliver consistent benefits regardless of the amount of available training data.
