Titlebook: Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology; A Multidisciplinary Maria Teresa Pazienza Conf

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Short query linguistic expansion techniques: Palliating one-word queries by providing intermediate tural language processing permit the extraction of typed information that is axed on one or two words. We review a selection of this typed information and describe how it could be used to present an intermediate structure for the user fitting between their short queries and the documents found in a heterogeneous text collection such as the WWW.

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Developing a Training Needs Assessment,that provides the right information to the right people at the right time in the right way. A training needs assessment can identify gaps in performance that lead to helpful recommendations of what to include in the final training solution. It is critical that learners’ needs are evaluated prior to

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Simulating LEGO Mindstorms Robots to Facilitate Teaching Computer Programming to School Studentsrojects that aim at increasing the interest in computer programming and computer science in general use robots as a cornerstone in their course concepts. Handing out robotic kits to the school students who have participated in the courses is not feasible, thus the learning content cannot be repeated

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1612-1317 synthesis of organic electrodes for advanced electrochemical.This book covers synthesis, properties, and applications of organic electrodes for advanced electrochemical applications. The future applications and challenges in using organic electrodes are also explored. The chapters describe their uni

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Introduction Overview of Wine Analysis, immediately after said event—for example, pre-commit, commit, and post-commit. In this chapter, you’ll have a look at what hooks are available in SVN, Git, and Mercurial, how you can enable hooks, and finally look at some examples of what you can do with hooks.

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Zur Beschleunigung des Lernens genetischer Algorithmen mittels unscharfer Regelsprachent werden, daß unscharfe Classifiersysteme theoretisch einen unendlichen Speed-Up im Vergleich zu klassischen Classifiersystemen Hefern. Sogar praktische Rechnerimplementierungen unscharfer Classifiersysteme, die ja unscharfe Classifiersysteme nur approximieren, lernen beträchtlich schneller als klassische Classifiersysteme.

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