Title :
Acronym Expansion Via Hidden Markov Models
Author :
Taghva, Kazem ; Vyas, Lakshmi
Author_Institution :
Sch. of Comput. Sci., Univ. of Nevada, Las Vegas, NV, USA
Abstract :
In this paper, we report on design and implementation of a Hidden Markov Model (HMM) to extract acronyms and their expansions. We also report on the training of this HMM with Maximum Likelihood Estimation (MLE) algorithm using a set of examples. Finally, we report on our testing using standard recall and precision. The HMM achieves a recall and precision of 98% and 92% respectively.
Keywords :
hidden Markov models; maximum likelihood estimation; acronym expansion; hidden Markov models; maximum likelihood estimation algorithm; Data mining; Databases; Hidden Markov models; Maximum likelihood estimation; Meteorology; Organic light emitting diodes; Terminology; Acronyms; HMM; Hidden Markov Models; MLE; Maximum Likelihood Estimation; supervised learning;
Conference_Titel :
Systems Engineering (ICSEng), 2011 21st International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4577-1078-0
DOI :
10.1109/ICSEng.2011.29
