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- W201537654 abstract "Generality is an attribute of text. Literally it means the degree of being general. The importance of generality in computer science cannot be ignored, especially in information retrieval and readability computation. Knowing how general a textual document is can help human compare documents retrieved, pick up the ones which can build up an overview of needed information, or select the ones which can remove the knowledge barrier and improve the understanding of professional information; knowing how general a textual document is can even be a factor to assist readability evaluation. However, the simulation of human generality judgement is a problem. The most important reason is that human's generality judgement is based on the understanding of text written in natural language. Until now, natural language processing is still an AI-complete problem. Secondly, the context sensitivity of generality judgement cannot be ignored. Finally, there is a lack of unit of measurement which can be used as a standard to measure document generality. In this thesis, a concept-based computational model is developed to estimate the semantic generality of documents with reference to domain ontology. In particular, the computation is based on the quantification of two basic characters of text, scope and cohesion. The scope of a document is regarded as the coverage of its terms onto the concepts in domain ontology. The more concepts matched within the ontology the more specific the document is. Also, within an ontology, the deeper the concepts appear, the more specific the document is. The cohesion of a document is regarded as a computation of the associations between the concepts found in the ontology. It reflects the frequencies of the associated concepts that appear in the ontology. The more closely the concepts are associated, the more specific the document is. The results of rigorous benchmark experiments confirm that the proposed semantic generality based IR model performs significantly better than the similarity-based baseline in both a bio-medical and an agricultural domain. In addition, a series of user-oriented studies reveal that the proposed document ranking functions resemble the implicit ranking functions exercised by humans. To the best of our knowledge, this is the first semantic generality based IR model developed to enhance domain specific IR." @default.
- W201537654 created "2016-06-24" @default.
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- W201537654 date "2011-01-01" @default.
- W201537654 modified "2023-09-24" @default.
- W201537654 title "Computational Generality in Information Retrieval" @default.
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