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Liu, S.; Liu, F.; Yu, C.; Meng, W.: ¬An effective approach to document retrieval via utilizing WordNet and recognizing phrases (2004)
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Wu, S.; Liu, S.; Wang, Y.; Timmons, T.; Uppili, H.; Bedrick, S.; Hersh, W.; Liu, H,: Intrainstitutional EHR collections for patient-level information retrieval (2017)
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Liu, S.: Decomposing DDC synthesized numbers (1996)
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- Abstract
- Much literature has been written speculating upon how classification can be used in online catalogs to improve information retrieval. While some empirical studies have been done exploring whether the direct use of traditional classification schemes designed for a manual environment is effective and efficient in the online environment, none has manipulated these manual classifications in such a w ay as to take full advantage of the power of both the classification and computer. It has been suggested by some authors, such as Wajenberg and Drabenstott, that this power could be realized if the individual components of synthesized DDC numbers could be identified and indexed. This paper looks at the feasibility of automatically decomposing DDC synthesized numbers and the implications of such decomposition for information retrieval. Based on an analysis of the instructions for synthesizing numbers in the main class Arts (700) and all DDC Tables, 17 decomposition rules were defined, 13 covering the Add Notes and four the Standard Subdivisions. 1,701 DDC synthesized numbers were decomposed by a computer system called DND (Dewey Number Decomposer), developed by the author. From the 1,701 numbers, 600 were randomly selected fo r examination by three judges, each evaluating 200 numbers. The decomposition success rate was 100% and it was concluded that synthesized DDC numbers can be accurately decomposed automatically. The study has implications for information retrieval, expert systems for assigning DDC numbers, automatic indexing, switching language development, enhancing classifiers' work, teaching library school students, and providing quality control for DDC number assignments. These implications were explored using a prototype retrieval system.
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Wei, F.; Li, W.; Liu, S.: iRANK: a rank-learn-combine framework for unsupervised ensemble ranking (2010)
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Deng, Z.; Deng, Z.; Fan, G.; Wang, B.; Fan, W.(P.); Liu, S.: More is better? : understanding the effects of online interactions on patients health anxiety (2023)
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Liu, S.; Chen, C.: ¬The differences between latent topics in abstracts and citation contexts of citing papers (2013)
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- Date
- 22. 3.2013 19:50:00
