a semantic similarity metric combining features and intrinsic information content

Intelligent Database Systems Lab

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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

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I. M.Outline

Motivation Objective Methodology Experiments Conclusion Comments

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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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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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I. M.Methodology Information theoretic approaches

Resnik Lin J&C

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Intelligent Database Systems Lab

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I. M.Methodology Ontology-based approaches

Rada et al. Hirst and St-Onge

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I. M.Methodology Hybrid approaches

Li et al. OSS

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I. M.Methodology

The P&S similarity metric

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I. M.Experiments

The P&S similarity experiment

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I. M.Experiments

The P&S similarity experiment

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Intelligent Database Systems Lab

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I. M.Experiments

The P&S similarity experiment

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I. M.Experiments

Evaluation and implementation of the P&S metric

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I. M.Experiments

The P&S similarity experiment

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I. M.Experiments

Impact of the intrinsic IC formulation

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I. M.Experiments The MeSH ontology

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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

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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