Using SVM with uneven margins to extract acronym-expansions

Extracting acronyms and their expansions from plain text is an important problem in text mining. Previous research work shows that it can be solved using machine learning approaches. That is, converting the problem of acronym extraction into a binary classification one. We investigated the classific...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 3; pp. 1286 - 1292
Main Authors Yong-Mei Gao, Ya-Lou Huang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Acronym Extraction
Cybernetics
Data mining
Information Extraction
Machine learning
Natural languages
Read only memory
Statistical learning
Support vector machine classification
Support vector machines
SVM
Text mining
Text recognition
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Summary:Extracting acronyms and their expansions from plain text is an important problem in text mining. Previous research work shows that it can be solved using machine learning approaches. That is, converting the problem of acronym extraction into a binary classification one. We investigated the classification problem and found that the classes are highly unbalanced. So we try to tackle the problem using SVM with uneven margins. Experimental results showed that our approach can get better results than baseline methods of using rules and conventional SVM models. Experimental results also showed how uneven margins classifier making the tradeoff between the extracting precision and recall.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212276