Intelligent Database Systems Lab N.Y.U.S.T. I. M. A semantic similarity metric combining features...

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


N.Y.U.S.T.

I. M.Outline

Motivation Objective Methodology Experiments Conclusion Comments

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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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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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N.Y.U.S.T.

I. M.Methodology

Information theoretic approaches Resnik

Lin

J&C

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N.Y.U.S.T.

I. M.Methodology

Ontology-based approaches Rada et al.

Hirst and St-Onge

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N.Y.U.S.T.

I. M.Methodology

Hybrid approaches Li et al.

OSS

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N.Y.U.S.T.

I. M.Methodology

The P&S similarity metric

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N.Y.U.S.T.

I. M.Experiments

The P&S similarity experiment

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N.Y.U.S.T.

I. M.Experiments

The P&S

similarity experiment

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N.Y.U.S.T.

I. M.Experiments


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N.Y.U.S.T.

I. M.Experiments

Evaluation and implementation of the P&S metric

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N.Y.U.S.T.

I. M.Experiments


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N.Y.U.S.T.

I. M.Experiments

Impact of the intrinsic IC formulation

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N.Y.U.S.T.

I. M.Experiments

The MeSH ontology

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N.Y.U.S.T.

I. M.Conclusion

1616

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.


N.Y.U.S.T.

I. M.Comments

1717

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

Intelligent Database Systems Lab N.Y.U.S.T. I. M. A semantic similarity metric combining features...

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