By Topic

Acronym extraction using SVM with Uneven Margins

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)

Extracting acronyms and their expansions from plain text is an important problem in text mining. Previous research shows that the problem can be solved via machine learning approaches. That is, converting the problem of acronym extraction to binary classification. We investigate the classification problem and find that the classes are highly unbalanced (the positive instances are very rare compared to negative ones). So we try to tackle the problem using an uneven margin classifier - SVM with Uneven Margins. Experimental results showed that our approach can get better results than baseline methods of using heuristic rules and conventional SVM models. Experimental results also showed how uneven margins classifier made the tradeoff between the precision and recall of extraction.

Published in:

2010 IEEE 2nd Symposium on Web Society

Date of Conference:

16-17 Aug. 2010