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On Defining Library and Information Science as Applied Philosophy of Information
"... This paper analyses the relations between philosophy of information (PI), library and information science (LIS) and social epistemology (SE). In the first section, it is argued that there is a natural relation between philosophy and LIS but that SE cannot provide a satisfactory foundation for LIS. ..."
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This paper analyses the relations between philosophy of information (PI), library and information science (LIS) and social epistemology (SE). In the first section, it is argued that there is a natural relation between philosophy and LIS but that SE cannot provide a satisfactory foundation for LIS. SE should rather be seen as sharing with LIS a common ground, represented by the study of information, to be investigated by a new discipline, PI. In the second section, the nature of PI is outlined as the philosophical area that studies the conceptual nature of information, its dynamics and problems. In the third section, LIS is defined as a form of applied PI. The hypothesis supported is that PI should replace SE as the philosophical discipline that can best provide the conceptual foundation for LIS. In the conclusion, it is suggested that the "identity" crisis undergone by LIS has been the natural outcome of a justified but precocious search for a philosophical counterpart that has emerged only recently, namely PI. The development of LIS should not rely on some borrowed, pre-packaged theory. As applied PI, LIS can fruitfully contribute to the growth of basic theoretical research in PI itself and thus provide its own foundation.
Text Augmentation: Inserting XML tags into natural language text with PPM Models and Viterbi-like search
, 2003
"... This thesis develops work on using Hidden Markov Models to insert tags natural language text. A taxonomy of tags is developed unifying the fields of text segmentation tagging, part-of-speech tagging, proper noun extraction and hierarchical entity extraction. The search spaces for inserting tags are ..."
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This thesis develops work on using Hidden Markov Models to insert tags natural language text. A taxonomy of tags is developed unifying the fields of text segmentation tagging, part-of-speech tagging, proper noun extraction and hierarchical entity extraction. The search spaces for inserting tags are examined from both a theoretical and experimental point of view across the taxonomy and on four corpora. A analysis of different correctness measures for different types of tag insertion problem is undertaken and a technique to determine whether tag-insertion errors are the result of a modelling failure or a searching failure is discovered.

