Learning to rank concept annotation for text
|Learning to rank concept annotation for text|
|Author(s)||Tu X., He T., Li F., Wang J.|
|Published in||Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis|
|Keyword(s)||Concept annotation, Explicit semantic analysis, Learning to ranking, Wikipedia|
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Learning to rank concept annotation for text is a 2013 journal article written in Chinese by Tu X., He T., Li F., Wang J. and published in Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis.
This paper proposed an automatic text annotation method (CRM, concept ranking model) based on learning to ranking model. Firstly the authors built a training set of concept annotation manualy, and then used the Ranking SVM algorithm to generate concept ranking model, finally the concept ranking model was used to generate concept annotation for any texts. Experiments show that proposed method has a significant improvement in various indicators compared to traditional annotation methods, and concept annotation results is closer to human annotation.
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