Acronym Expansion via Hidden Markov Models
21st International Conference on Systems Engineering
Las Vegas, NV
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.
Acronym expansion; Acronyms; Hidden Markov models; HMM; Maximum likelihood estimation; Maximum likelihood estimation algorithm; MLE; Pattern recognition systems; Supervised learning
Computer Engineering | Electrical and Computer Engineering | Software Engineering
Acronym expansion; Acronyms; Hidden Markov models; HMM; Maximum likelihood estimation; Maximum likelihood estimation algorithm; MLE; Supervised learning
Acronym Expansion via Hidden Markov Models.
Presentation at 21st International Conference on Systems Engineering,
Las Vegas, NV.
Available at: https://digitalscholarship.unlv.edu/ece_presentations/19