1 / 17

A semantic similarity metric combining features and intrinsic information content

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

ingo
Download Presentation

A semantic similarity metric combining features and intrinsic information content

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related