a semantic similarity metric combining features and intrinsic information content
DESCRIPTION
A semantic similarity metric combining features and intrinsic information content. Presenter: Chun-Ping Wu Author: Giuseppe Pirro. 國立雲林科技大學 National Yunlin University of Science and Technology. 2011/01/05. DKE 2009. Outline. Motivation Objective Methodology Experiments Conclusion - PowerPoint PPT PresentationTRANSCRIPT
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
A semantic similarity metric combining features and intrinsic information content
Presenter: Chun-Ping Wu Author: Giuseppe Pirro
DKE 2009
國立雲林科技大學National Yunlin University of Science and Technology
2011/01/05
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Outline
Motivation Objective Methodology Experiments Conclusion Comments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Motivation
In many research fields, computing semantic similarity between words is an important issue.
The previous methods have some drawbacks.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Objective
To propose a new similarity metric(P&S) to solve the shortcomings of existing approaches. The P&S metric neither require complex IC computations nor
configuration knobs to be adjusted.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology Information theoretic approaches
Resnik Lin J&C
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology Ontology-based approaches
Rada et al. Hirst and St-Onge
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology Hybrid approaches
Li et al. OSS
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
The P&S similarity metric
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
The P&S similarity experiment
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
The P&S similarity experiment
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
The P&S similarity experiment
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
Evaluation and implementation of the P&S metric
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
The P&S similarity experiment
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
Impact of the intrinsic IC formulation
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments The MeSH ontology
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Conclusion
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This paper solves the shortcomings of the previous studies. The P&S metric neither require complex IC computations nor
configuration knobs to be adjusted.
This metric, as shown by experimental evaluation, outperforms the state of the art.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Comments
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Advantage This paper solves the shortcomings of the previous studies. There are many experiments in this paper.
Drawback It still needs an ontology
Application Semantic similarity, WSD