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Memory-Based Learning (MBL), one of the most influential machine learning paradigms, has been applied with great success to a variety of NLP tasks. This monograph describes the application of MBL to robust parsing. Robust parsing using MBL can provide added functionality for key NLP applications, such as Information Retrieval, Information Extraction, and Question Answering, by facilitating more complex syntactic analysis than is currently available. The text presupposes no prior knowledge of MBL. It provides a comprehensive introduction to the framework and goes on to describe and compare applications of MBL to parsing. Since parsing is not easily characterizable as a classification task, adaptations of standard MBL are necessary. These adaptations can either take the form of a cascade of local classifiers or of a holistic approach for selecting a complete tree.The text provides excellent course material on MBL. It is equally relevant for any researcher concerned with symbolic machine learning, Information Retrieval, Information Extraction, and Question Answering.
The text presupposes no prior knowledge of MBL. It provides a comprehensive introduction to the framework and goes on to describe and compare applications of MBL to parsing.
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
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.
While there is no deficit of religious intelligence, there is a need for spiritual understanding. In essence, there is an intelligence that comprehends the divine-human connection with all of its privileges, promises, and responsibilities. This book is based upon this premise. Spiritual Intelligence is the ability to understand truths, facts, meanings, relationships, functions, and realities that are seen, unseen, heard and not heard. It is a privilege of the salvation experience that can be developed. While academic intelligence measures the ability to comprehend science, language, mathematics, and logical reasoning, it is not an adequate measurement of the capacity to understand information beyond the natural and sensory spheres. Spiritual Intelligence is an acquired sensitivity, awareness, and consciousness of the existence and integration of the divine-human connection.
This book is based upon this premise. Spiritual Intelligence is the ability to understand truths, facts, meanings, relationships, functions, and realities that are seen, unseen, heard and not heard.
