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12 result(s) for "Reading machines (Data processing equipment)"
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Reading Machines
Besides familiar and now-commonplace tasks that computers do all the time, what else are they capable of? Stephen Ramsay's intriguing study of computational text analysis examines how computers can be used as \"reading machines\" to open up entirely new possibilities for literary critics. Computer-based text analysis has been employed for the past several decades as a way of searching, collating, and indexing texts. Despite this, the digital revolution has not penetrated the core activity of literary studies: interpretive analysis of written texts._x000B__x000B_Computers can handle vast amounts of data, allowing for the comparison of texts in ways that were previously too overwhelming for individuals, but they may also assist in enhancing the entirely necessary role of subjectivity in critical interpretation. Reading Machines discusses the importance of this new form of text analysis conducted with the assistance of computers. Ramsay suggests that the rigidity of computation can be enlisted by intuition, subjectivity, and play.
Ex-foliations
Terry Harpold investigates paradoxes of reading’s backward glances in the theory and literature of the digital field. In analyses of Vannevar Bush’s Memex and Ted Nelson’s Xanadu, and in readings of hypertext fictions by Michael Joyce and Shelley Jackson, Harpold asserts that we should return to these landmarks of new media scholarship with attention on questions of media obsolescence, changing user interface designs, and the mutability of reading.
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference
Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. The authors present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, they have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. The authors describe several challenges and their solutions in designing, implementing, and deploying Elementary. In particular, the authors first describe the conceptual framework and architecture of Elementary to integrate different data resources and KBC techniques in a principled manner. They then discuss how they address scalability challenges to enable Web-scale deployment. The authors empirically show that this decomposition-based inference approach achieves higher performance than prior inference approaches. To validate the effectiveness of Elementary’s approach to KBC, they experimentally show that its ability to incorporate diverse signals has positive impacts on KBC quality.
Emergency control of technological environment and electric machinery activity in coal mines
The authors develop standards and design of pre-emergency, emergency and post-emergency control of mine technological environment and electric machinery activity. The electric machinery operation parameters to be controlled are energy supply of explosion-proof electric machinery, cable integrity and improper access to the machinery; the control objects are switchers and starters within an extraction district. The technological environment parameters to be controlled are gas content, including CH 4 , CO, CO 2 and O 2 , variation in mine air pressure, light, sound, temperature, acceleration or dislocation of central control unit housing. The article presents engineering solutions on subsystems of data reading, processing and storage.
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Tulsa World, Okla., Action Line column
Emancipation Day is observed as a federal holiday in Washington, D.C. As a result, explained Internal Revenue Service spokesman David Stell, taxpayers have until Tuesday, April 17, to file their 2011 tax returns and pay any tax due.
Rating news documents for similarity
Electronic news has long held the promise of personalized and dynamic delivery of current event news items, particularly for web users. Although electronic versions of print news are now widely available, the personalization of that delivery has not yet been accomplished. In this paper, we present a methodology of associating news documents based on the extraction of feature phrases, where feature phrases identify dates, locations, people, and organizations. A news representation is created from these feature phrases to define news objects that can then be compared and ranked to find related news items. Unlike traditional information retrieval, we are much more interested in precision than recall. That is, the user would like to see one or more specifically related articles, rather than all somewhat related articles. The algorithm is designed to work interactively with the user using regular web browsers as the interface.