Soft Computing

, Volume 11, Issue 4, pp 369-373

First online:

Using SVM to Extract Acronyms from Text

  • Jun XuAffiliated withCollege of Software, Nankai University Email author 
  • , Yalou HuangAffiliated withCollege of Software, Nankai University

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


The paper addresses the problem of extracting acronyms and their expansions from text. We propose a support vector machines (SVM) based approach to deal with the problem. First, all likely acronyms are identified using heuristic rules. Second, expansion candidates are generated from surrounding text of acronyms. Last, SVM model is employed to select the genuine expansions. Analysis shows that the proposed approach has the advantages of saving over the conventional rule based approaches. Experimental results show that our approach outperforms the baseline method of using rules. We also show that the trained SVM model is generic and can adapt to other domains easily.


Acronym Expansion Classification Support vector machines