1# Library to Find Best eBook for Read
Author : Juntao Yu
Release : 2018
Genre :
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Semi-supervised Methods for Out-of-domain Dependency Parsing by : Juntao Yu
Download or read book Semi-supervised Methods for Out-of-domain Dependency Parsing written by Juntao Yu. This book was released on 2018. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Wenliang Chen
Release : 2015-07-16
Genre : Language Arts & Disciplines
Kind : eBook
Book Rating : 522/5 ( reviews)
Book Synopsis Semi-Supervised Dependency Parsing by : Wenliang Chen
Download or read book Semi-Supervised Dependency Parsing written by Wenliang Chen. This book was released on 2015-07-16. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Anders Søgaard
Release : 2022-05-31
Genre : Computers
Kind : eBook
Book Rating : 495/5 ( reviews)
Book Synopsis Semi-Supervised Learning and Domain Adaptation in Natural Language Processing by : Anders Søgaard
Download or read book Semi-Supervised Learning and Domain Adaptation in Natural Language Processing written by Anders Søgaard. This book was released on 2022-05-31. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Sandra Kübler
Release : 2009
Genre : Computers
Kind : eBook
Book Rating : 969/5 ( reviews)
Book Synopsis Dependency Parsing by : Sandra Kübler
Download or read book Dependency Parsing written by Sandra Kübler. This book was released on 2009. Available in PDF, EPUB and Kindle. Book excerpt: 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
Author : Marzieh Razavi
Release : 2012
Genre : Cluster analysis
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Ensembles of Diverse Clustering-based Discriminative Dependency Parsers by : Marzieh Razavi
Download or read book Ensembles of Diverse Clustering-based Discriminative Dependency Parsers written by Marzieh Razavi. This book was released on 2012. Available in PDF, EPUB and Kindle. Book excerpt: Syntactic parsing and dependency parsing in particular are a core component of many Natural Language Processing (NLP) tasks and applications. Improvements in dependency parsing can help improve machine translation and information extraction applications among many others. In this thesis, we extend the framework of (Koo, Carreras, and Collins, 2008) for dependency parsing which uses a single clustering method for semi-supervised learning. We make use of multiple diverse clustering methods to build multiple discriminative dependency parsing models in the Maximum Spanning Tree (MST) parsing framework (McDonald, Crammer, and Pereira, 2005). All of these diverse clustering-based parsers are then combined together using a novel ensemble model, which performs exact inference on the shared hypothesis space of all the parser models. We show that diverse clustering-based parser models and the ensemble method together significantly improves unlabeled dependency accuracy from 90.82% to 92.46% on Section 23 of the Penn Treebank. We also show significant improvements in domain adaptation to the Switchboard and Brown corpora.
