基于隐马尔可夫模型的缩略语展开

2011 21st International Conference on Systems Engineering Pub Date : 2011-08-16 DOI:10.1109/ICSEng.2011.29

K. Taghva, Lakshmi Vyas

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引用次数: 7

摘要

在本文中，我们报告了一个隐马尔可夫模型(HMM)的设计和实现，用于提取缩略语及其展开。我们还报告了使用最大似然估计(MLE)算法使用一组示例训练该HMM。最后，我们报告了使用标准召回率和精确率进行的测试。HMM的查全率和查准率分别达到98%和92%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Acronym Expansion Via Hidden Markov Models

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.

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2011 21st International Conference on Systems Engineering

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