Using SVM to Extract Acronyms from Text

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.