Title :
A machine learning approach to recognizing acronyms and their expansion
Author :
Jun Xu ; Ya-Lou Huang
Author_Institution :
Coll. of Software, Nankai Univ., Tianjin, China
Abstract :
The paper addresses the problem of automatically recognizing acronyms and their corresponding expansions in free format text. To deal with the problem, we propose a machine learning approach. First, all likely acronyms are identified from text. Second, candidate expansions against likely acronyms are generated from their surrounding text. Last, we employ support vector machines (SVM) to select the genuine expansions for acronyms. Experimental results show that our approach outperforms baseline method of using patterns. Experimental results also show that the trained SVM model is generic and performs well on different domains.
Keywords :
data mining; information retrieval; learning (artificial intelligence); pattern recognition; support vector machines; text analysis; acronyms expansion; acronyms recognition; machine learning approach; support vector machine; text mining; Educational institutions; Information retrieval; Internet; Machine learning; Natural languages; Read only memory; Search engines; Support vector machines; Text mining; Text recognition; Acronym extraction; expansion; machine learning; text mining;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527330