The Geometry of Information Retrieval
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Important work on a new framework for information retrieval: implications for artificial intelligence, natural language processing.Information retrieval, IR, the science of extracting information from any potential source, can be viewed in a number of ways: logical, probabilistic and vector space models are some of the most important. In this book, the author, one of the leading researchers in the area, shows how these views can be reforged in the same framework used to formulate the general principles of quantum mechanics. All the usual quantum-mechanical notions have their IR-theoretic analo...
Important work on a new framework for information retrieval: implications for artificial intelligence, natural language processing.
Information retrieval, IR, the science of extracting information from any potential source, can be viewed in a number of ways: logical, probabilistic and vector space models are some of the most important. In this book, the author, one of the leading researchers in the area, shows how these views can be reforged in the same framework used to formulate the general principles of quantum mechanics. All the usual quantum-mechanical notions have their IR-theoretic analogues, and the standard results can be applied to address problems in IR, such as pseudo-relevance feedback, relevance feedback and ostensive retrieval. The relation with quantum computing is also examined. To keep the book self-contained appendices with background material on physics and mathematics are included. Each chapter ends with bibliographic remarks that point to further reading. This is an important, ground-breaking book, with much new material, for all those working in IR, AI and natural language processing.
Table of content:
Preface; Prologue; 1. Introduction; 2. On sets and kinds in IR; 3. Vector and Hilbert spaces; 4. Linear transformations, operators and matrices; 5. Conditional logic in IR; 6. The geometry of IR; Appendix I. Linear algebra; Appendix II. Quantum mechanics; Appendix III. Probability; Bibliography; Index.
Information retrieval, IR, the science of extracting information from any potential source, can be viewed in a number of ways: logical, probabilistic and vector space models are some of the most important. In this book, the author, one of the leading researchers in the area, shows how these views can be reforged in the same framework used to formulate the general principles of quantum mechanics. All the usual quantum-mechanical notions have their IR-theoretic analogues, and the standard results can be applied to address problems in IR, such as pseudo-relevance feedback, relevance feedback and ostensive retrieval. The relation with quantum computing is also examined. To keep the book self-contained appendices with background material on physics and mathematics are included. Each chapter ends with bibliographic remarks that point to further reading. This is an important, ground-breaking book, with much new material, for all those working in IR, AI and natural language processing.
Table of content:
Preface; Prologue; 1. Introduction; 2. On sets and kinds in IR; 3. Vector and Hilbert spaces; 4. Linear transformations, operators and matrices; 5. Conditional logic in IR; 6. The geometry of IR; Appendix I. Linear algebra; Appendix II. Quantum mechanics; Appendix III. Probability; Bibliography; Index.
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