doi: 10.7763/IJMLC.2012.V2.111
Measuring Word Similarity Based on the Optimal Transformation and Combination of Features
ChukFong Ho
,
Masrah Azrifah Azmi-Murad ,
Shyamala Doraisamy ,
Rabiah Abdul-Kadir
- Faculty of Computer Science and Information Technology, Selangor, Malaysia.
Abstract
The semantic similarity between two words can be determined based on their common and distinctive features after transforming them into measurable values. Up to now, a variety of transfer functions with respect to the transformation and the combination of features have been proposed. However, none of them have ever processed and combined those features properly, thus making them incapable of making a judgment of similarity that is close to human judgments of similarity. This paper offers a method that represents the optimal combination of the optimal transformation of two types of widely used features. The results obtained from a standard data set show that the proposed solution outperforms all of its benchmarks significantly.
Keywords
- Semantic similarity
- transfer function
- word similarity
How to Cite
ChukFong Ho, Masrah Azrifah Azmi-Murad, Shyamala Doraisamy, and Rabiah Abdul-Kadir, "Measuring Word Similarity Based on the Optimal Transformation and Combination of Features," International Journal of Machine Learning, vol. 2, no. 3, pp. 188-194, 2012. https://doi.org/10.7763/IJMLC.2012.V2.111
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).