http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Synthesis digital library of engineering and computer science
1 online resource (xvii, 119 pages) : illustrations
The Answer Machine is a practical, non-technical guide to the technologies behind information seeking and analysis. It introduces search and content analytics to software buyers, knowledge managers, and searchers who want to understand and design effective online environments. The book describes how search evolved from an expert-only to an end user tool. It provides an overview of search engines, categorization and clustering, natural language processing, content analytics, and visualization technologies. Detailed profiles for Web search, eCommerce search, eDiscovery, and enterprise search contrast the types of users, uses, tasks, technologies, and interaction designs for each. These variables shape each application, although the underlying technologies are the same. Types of information tasks and the trade-offs between precision and recall, time, volume and precision, and privacy vs. personalization are discussed within this context. The book examines trends toward convenient, context-aware computing, big data and analytics technologies, conversational systems, and answer machines. The Answer Machine explores IBM Watson's Deep QA technology and describes how it is used to answer health care and Jeopardy questions. The book concludes by discussing the implications of these advances: how they will change the way we run our businesses, practice medicine, govern, or conduct our lives in the digital age.
1. Introduction -- 1.1 Why information seeking is so important to us --
2. The query process and barriers to finding information online -- 2.1 The query process -- 2.2 Query problems --
3. Online search: an evolution -- 3.1 History -- 3.2 The shifting information landscape: 2000-2012 -- 3.2.1 End users as searchers -- 3.2.2 Changes in information seeking -- 3.2.3 State of the art today --
4. Search and discovery technologies: an overview -- 4.1 Online information access systems -- 4.2 Search and discovery technologies -- 4.3 Types of search systems -- 4.4 The information retrieval process -- 4.5 Search and content analytics -- 4.6 Collecting information for searching or analysis -- 4.7 Search engines: the index and the matching engine -- 4.8 Presenting the results: what is "relevance?" -- 4.8.1 Beyond the document list -- 4.9 Categorization, classification, clustering, and faceted search -- 4.9.1 Automatic vs. manual categorization -- 4.10 Natural language processing (NLP) and content analytics -- 4.11 Time, sentiment, and geo-location -- 4.12 NLP in information retrieval -- 4.12.1 Some common uses of NLP -- 4.13 Multilingual and cross language search, gisting, and translation -- 4.14 Knowledge bases -- 4.14.1 Rich media search --
5. Information access: a spectrum of needs and uses -- 5.1 Information tasks -- 5.2 Information seekers -- 5.3 Finding the right search technology -- 5.3.1 First questions -- 5.3.2 Information needs assessment checklist -- 5.4 Trade-offs in search and content technologies -- 5.5 Search and content analytics technologies: sample use cases -- 5.5.1 Web search -- 5.5.2 eCommerce search -- 5.5.3 eDiscovery search -- 5.5.4 Enterprise search -- 5.6 Search and content analytics in the enterprise -- 5.6.1 Consumer vs. business search -- 5.7 Trends in enterprise search -- 5.7.1 Unified information access -- 5.7.2 InfoApps and search-based applications -- 5.7.3 Opinion,trend, and sentiment monitoring -- 5.7.4 Question answering systems -- 5.7.5 Site search -- 5.7.6 Mobile search -- 5.8 Enterprise search systems in summary --
6. Future tense: the next era in information access and discovery -- 6.1 Shift to probabilistic computing -- 6.2 Learning systems: machine learning, adaptive systems, predictive analytics, and inferencing -- 6.3 Big data and analytics -- 6.4 Improved information interaction: contextual awareness, conversational systems, and visualization -- 6.5 Complex, highly integrated information platforms --
7. Answer machines -- 7.1 What's an answer machine? -- 7.1.1 Question definition -- 7.1.2 Interaction design -- 7.1.3 Analytics and adaptive learning -- 7.1.4 Complex, highly integrated information platforms -- 7.2 IBM's Watson: an answer machine case study -- 7.2.1 Watson for Jeopardy -- 7.2.2 What's under the hood? -- 7.3 Answer machines and the future -- 7.3.1 What answer machines can't do -- 7.3.2 Implications -- 7.4 Conclusion --
Bibliography -- Author's biography -- Index.
Includes bibliographical references (pages 111-114) and index.
COMPUTERS / Enterprise Applications / Business Intelligence Tools
COMPUTERS / Intelligence (AI) & Semantics
Synthesis lectures on information concepts, retrieval, and services ; #23