Nitin Indurkhya – författare

The growth of the web can be seen as an expanding public digital library collection. Online digital information extends far beyond the web and its publicly available information. Huge amounts of information are private and are of interest to local communities, such as the records of customers of a business. This information is overwhelmingly text and has its record-keeping purpose, but an automated analysis might be desirable to find patterns in the stored records. Analogous to this data mining is text mining, which also finds patterns and trends in information samples but which does so with far less structured--though with greater immediate utility for users--ingredients. This book focuses on the concepts and methods needed to expand horizons beyond structured, numeric data to automated mining of text samples. It introduces the new world of text mining and examines proven methods for various critical text-mining tasks, such as automated document indexing and information retrieval and search. New research areas are explored, such as information extraction and document summarization, that rely on evolving text-mining techniques.

A fascinating shift marks the journey of technological evolution. Historically, humans were trained to build and operate machines. This education emphasised mechanical skills, logical thinking, and problem-solving abilities, laying the groundwork for the following industrial revolutions. Early machines, from the steam engine to the assembly line, were designed and maintained by skilled human workers, reflecting a one-way relationship where humans were the creators and controllers of machines.Today, we stand on the brink of a new paradigm. Advanced artificial intelligence (AI) systems and generative AI tools are not just aiding human tasks but are also capable of educating and guiding humans. These intelligent machines can analyse vast amounts of data, provide personalised learning experiences, and generate creative content. The transformation from humans building machines to machines educating humans signifies a profound shift in our technological landscape, impacting industries.As editors, we are united by a profound conviction that bridging the gap between industry and higher education is imperative in the face of rapid advancements in generative AI and innovations across various industries. This connection is crucial to equipping graduates and young professionals with the skills to innovate and excel in the future workforce. Through this multidisciplinary exploration, The Rise of Intelligent Machines aims to provide readers with an understanding of AI's transformative potential and the strategies needed to harness its benefits for industry and education.Every chapter reflects our shared passion for AI’s potential and close industry-higher education collaboration, and we hope that our work inspires you to embrace the opportunities and challenges of the future with curiosity and confidence.Cover design by Ernest Tey

A fascinating shift marks the journey of technological evolution. Historically, humans were trained to build and operate machines. This education emphasised mechanical skills, logical thinking, and problem-solving abilities, laying the groundwork for the following industrial revolutions. Early machines, from the steam engine to the assembly line, were designed and maintained by skilled human workers, reflecting a one-way relationship where humans were the creators and controllers of machines.Today, we stand on the brink of a new paradigm. Advanced artificial intelligence (AI) systems and generative AI tools are not just aiding human tasks but are also capable of educating and guiding humans. These intelligent machines can analyse vast amounts of data, provide personalised learning experiences, and generate creative content. The transformation from humans building machines to machines educating humans signifies a profound shift in our technological landscape, impacting industries.As editors, we are united by a profound conviction that bridging the gap between industry and higher education is imperative in the face of rapid advancements in generative AI and innovations across various industries. This connection is crucial to equipping graduates and young professionals with the skills to innovate and excel in the future workforce. Through this multidisciplinary exploration, The Rise of Intelligent Machines aims to provide readers with an understanding of AI's transformative potential and the strategies needed to harness its benefits for industry and education.Every chapter reflects our shared passion for AI’s potential and close industry-higher education collaboration, and we hope that our work inspires you to embrace the opportunities and challenges of the future with curiosity and confidence.Cover design by Ernest Tey

The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second Edition Greater prominence of statistical approachesNew applications sectionBroader multilingual scope to include Asian and European languages, along with EnglishAn actively maintained wiki (http://handbookofnlp.cse.unsw.edu.au) that provides online resources, supplementary information, and up-to-date developmentsDivided into three sections, the book first surveys classical techniques, including both symbolic and empirical approaches. The second section focuses on statistical approaches in natural language processing. In the final section of the book, each chapter describes a particular class of application, from Chinese machine translation to information visualization to ontology construction to biomedical text mining. Fully updated with the latest developments in the field, this comprehensive, modern handbook emphasizes how to implement practical language processing tools in computational systems.

The growth of the web can be seen as an expanding public digital library collection. Online digital information extends far beyond the web and its publicly available information. Huge amounts of information are private and are of interest to local communities, such as the records of customers of a business. This information is overwhelmingly text and has its record-keeping purpose, but an automated analysis might be desirable to find patterns in the stored records. Analogous to this data mining is text mining, which also finds patterns and trends in information samples but which does so with far less structured--though with greater immediate utility for users--ingredients. This book focuses on the concepts and methods needed to expand horizons beyond structured, numeric data to automated mining of text samples. It introduces the new world of text mining and examines proven methods for various critical text-mining tasks, such as automated document indexing and information retrieval and search. New research areas are explored, such as information extraction and document summarization, that rely on evolving text-mining techniques.

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics.