Share

Ensembles of Diverse Clustering-based Discriminative Dependency Parsers

Download Ensembles of Diverse Clustering-based Discriminative Dependency Parsers PDF Online Free

Author :
Release : 2012
Genre : Cluster analysis
Kind : eBook
Book Rating : /5 ( reviews)

GET EBOOK


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.

Semi-Supervised Dependency Parsing

Download Semi-Supervised Dependency Parsing PDF Online Free

Author :
Release : 2015-07-16
Genre : Language Arts & Disciplines
Kind : eBook
Book Rating : 522/5 ( reviews)

GET EBOOK


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.

Improving Dependency Parsing Using Word Clusters

Download Improving Dependency Parsing Using Word Clusters PDF Online Free

Author :
Release : 2015-10-22
Genre :
Kind : eBook
Book Rating : 551/5 ( reviews)

GET EBOOK


Book Synopsis Improving Dependency Parsing Using Word Clusters by : Jostein Lien

Download or read book Improving Dependency Parsing Using Word Clusters written by Jostein Lien. This book was released on 2015-10-22. Available in PDF, EPUB and Kindle. Book excerpt: Several studies have attempted to improve the accuracy in dependency parsing by including information about word clusters into the parsing models. The use of word clusters are typically motivated by the shortage of labeled training data and domain adaption, attempting to influence a parsing model for use on data from a new domain. This book shows the effect of using cluster-based features in MaltParser, a data-driven parser for inductive dependency parsing. Different clustering features are used for generating clusters, using the K-means clustering algorithm. The clusters are used as a source of additional information in an expanded feature model used by the MaltParser system. Parsing experiments are performed on several different data sets, including the Wall Street Journal and texts from various web domains. Significantly improved parsing results are reported when using a cluster-informed parser compared to the baseline parser. The contents of this book might be of interest to anyone interested in the application of machine learning in language technology.

Advances in Discriminative Dependency Parsing

Download Advances in Discriminative Dependency Parsing PDF Online Free

Author :
Release : 2010
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

GET EBOOK


Book Synopsis Advances in Discriminative Dependency Parsing by : Terry Y. Koo

Download or read book Advances in Discriminative Dependency Parsing written by Terry Y. Koo. This book was released on 2010. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Big Data Analytics Methods

Download Big Data Analytics Methods PDF Online Free

Author :
Release : 2019-12-16
Genre : Business & Economics
Kind : eBook
Book Rating : 567/5 ( reviews)

GET EBOOK


Book Synopsis Big Data Analytics Methods by : Peter Ghavami

Download or read book Big Data Analytics Methods written by Peter Ghavami. This book was released on 2019-12-16. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

You may also like...