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= SBA-term: Sparse Bilingual Association for Terms

- *Download:* [Pubs/icsc2011.pdf .pdf]

- *Authors:* Xinyu Dai and Jinzhu Jia and Laurent El Ghaoui and Bin Yu.

- *Status:* Accepted for publication in /Proc. [http://www.ieee-icsc.org/ICSC2011/ Fifth IEEE International Conference on Semantic Computing]/, September 2011.

- *Abstract:*  Bilingual semantic term association is very useful in cross-language information retrieval, statistical machine translation, and many other applications in natural language processing. In this paper, we present a method, named SBA-term,  which applies sparse linear regression (Lasso, Least Squares with $l_1$ penalty) and $l^{2}$ rescaling for design matrix to the task of bilingual term association. The approach hinges on formulating the task as a feature selection problem within a classification framework. Our experimental results indicate that our novel proposed method is more efficient than co-occurrence at extracting relevant bilingual terms semantic associations. In addition, our approach connects the vibrant area of sparse machine learning to an important problem of natural language processing.

#- *Related links:*

- *Bibtex reference:*
~~~
{}{}
@inproceedings{DJEB:11, 
   Author = {X. Dai and J. Jia and L. {El Ghaoui} and B. Yu},
   Title = {{SBA}-term: Sparse Bilingual Association for Terms},
   BookTitle = {Fifth IEEE International Conference on Semantic Computing},
   Address= {Palo Alto, CA, USA},
	 Month = sep,
   Year = {2011} 
}
~~~