A Ranking SVM Based Algorithm for Automatic Extraction of Acronym
GAO YongMei1, HUANG YaLou2, NI WeiJian1, XU Jun3
1.College of Information Technical Science, Nankai University, Tianjin 3000712. College of Software, Nankai University, Tianjin 3000713. Microsoft Research Asia, Beijing 100000
Abstract：A ranking SVM based method is proposed for automatically extracting acronyms and their corresponding expansions in free format text. A twostep ranking model is established, including local extraction and global ranking. For each extracted acronym, the model returns to an ordered list of expansion candidates, where the candidates are ranked according to their correctness and the degree of popularity. The ranking model can effectively eliminate noise and help user find the true expansions. Experimental results on real data validate its higher performance and better general adaptation in different domains than other methods.
2008, Vol. 21 
