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International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
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ISBN:
9780791859902
No. of Pages:
1400
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
309 Evaluating Semantic Relatedness Using Wikipedia-Based Representative Features Analysis
By
Qing-jun Cui
State Key Laboratory of Software Development Environment, Beihang University , Beijing , China ; cuiqj@nlsde.buaa.edu.cn
,
Qing-jun Cui
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Hui Zhang
State Key Laboratory of Software Development Environment, Beihang University , Beijing , China ; hzhang@nlsde.buaa.edu.cn
,
Hui Zhang
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Rui Liu
State Key Laboratory of Software Development Environment, Beihang University , Beijing , China ; liurui@nlsde.buaa.edu.cn
Rui Liu
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Page Count:
6
-
Published:2011
Citation
Cui, Q, Zhang, H, & Liu, R. "Evaluating Semantic Relatedness Using Wikipedia-Based Representative Features Analysis." International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011). Ed. Ming, C. ASME Press, 2011.
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In order to evaluate semantic relatedness of natural language concepts automatically, we propose Representative Features Analysis (RFA), a novel approach that represents the meaning of concepts in a high-dimensional space of representative features as a semantic-surrounding concept vector. The vector elements are weighted by the combination of TF-IDF scheme and the link status of Concept Interpreting Network in which nodes represent the concepts and edges represent the interpreting relation between concepts. Assessing the relatedness amounts to comparing the corresponding vectors using conventional metrics. Compared with the previous state of the art, using RFA results in substantial improvements in correlation of...
Topics:
Radiofrequency ablation
Abstract
Keywords:
Introduction
Related Work
Representative Features Analysis
Evaluation
Conclusions and Further Work
Acknowledgment
References
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