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A semantic similarity metric combining features and intrinsic information content

This paper presents a novel semantic similarity metric, termed P&S, aimed at addressing the limitations of existing methods in computing semantic similarity between words. The P&S metric simplifies the process by eliminating the need for complex intrinsic computation and configuration adjustments. Through a series of experiments, including evaluations on the MeSH ontology, the proposed metric demonstrates superior performance over traditional state-of-the-art methods. This study contributes to various fields where semantic similarity plays a crucial role.

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A semantic similarity metric combining features and intrinsic information content

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  1. 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

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation • In many research fields, computing semantic similarity between words is an important issue. • The previous methods have some drawbacks.

  4. 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.

  5. Methodology • Information theoretic approaches • Resnik • Lin • J&C

  6. Methodology • Ontology-based approaches • Rada et al. • Hirst and St-Onge

  7. Methodology • Hybrid approaches • Li et al. • OSS

  8. Methodology • The P&S similarity metric

  9. Experiments • The P&S similarity experiment

  10. Experiments • The P&S similarity experiment

  11. Experiments • The P&S similarity experiment

  12. Experiments • Evaluation and implementation of the P&S metric

  13. Experiments • The P&S similarity experiment

  14. Experiments • Impact of the intrinsic IC formulation

  15. Experiments • The MeSH ontology

  16. Conclusion • 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. 16

  17. Comments • 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 17

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