Algorithmic Reading Comprehension
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Algorithmic Reading Comprehension
Author: Rahul Anand
language: en
Publisher: Educreation Publishing
Release Date: 2018-10-10
Reading comprehension solving is a skill. It is different from literature in the sense that it is independent of interpretation. There is no subjectivity in RC questions. The art of solving can be learnt by mastering the topic at three levels – The ability to read, to eliminate options and to build mistake patterns to learn from them. These three levels lie at the core of "Algorithmic Reading Comprehension" – an approach to build expertise in RC solving. This approach was created by my team and me over the past 6 years of training thousands of students for the Reading comprehension section for CAT-GMAT and other aptitude-based entrance examinations. The book deals with two major aspects of reading, "Central Idea" and "Contextual Word Learning". It moves on to discuss the meaning of different question types asked across exams and provides elimination frameworks to tackle tricky options. Finally, students get many passages arranged in levels and then in a practice chapter to practice and to learn from. Welcome to the world of flawless RC learning!
Machine Reading Comprehension
Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in several public datasets. It has also been widely deployed by industry in search engine and quality assurance systems. Machine Reading Comprehension: Algorithms and Practice performs a deep-dive into MRC, offering a resource on the complex tasks this technology involves. The title presents the fundamentals of NLP and deep learning, before introducing the task, models, and applications of MRC. This volume gives theoretical treatment to solutions and gives detailed analysis of code, and considers applications in real-world industry. The book includes basic concepts, tasks, datasets, NLP tools, deep learning models and architecture, and insight from hands-on experience. In addition, the title presents the latest advances from the past two years of research. Structured into three sections and eight chapters, this book presents the basis of MRC; MRC models; and hands-on issues in application. This book offers a comprehensive solution for researchers in industry and academia who are looking to understand and deploy machine reading comprehension within natural language processing. - Presents the first comprehensive resource on machine reading comprehension (MRC) - Performs a deep-dive into MRC, from fundamentals to latest developments - Offers the latest thinking and research in the field of MRC, including the BERT model - Provides theoretical discussion, code analysis, and real-world applications of MRC - Gives insight from research which has led to surpassing human parity in MRC
Concept Parsing Algorithms (CPA) for Textual Analysis and Discovery: Emerging Research and Opportunities
Text analysis tools aid in extracting meaning from digital content. As digital text becomes more and more complex, new techniques are needed to understand conceptual structure. Concept Parsing Algorithms (CPA) for Textual Analysis and Discovery: Emerging Research and Opportunities provides an innovative perspective on the application of algorithmic tools to study unstructured digital content. Highlighting pertinent topics such as semantic tools, semiotic systems, and pattern detection, this book is ideally designed for researchers, academics, students, professionals, and practitioners interested in developing a better understanding of digital text analysis.