A Machine Learning Approach to Acronym Generation
A Machine Learning Approach to Acronym Generation
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DOI：
10.3115/1641484.1641488
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发表时间：
2005-06
期刊：
影响因子：
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通讯作者：
Yoshimasa Tsuruoka;S. Ananiadou;Junichi Tsujii
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文献类型：
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作者：
Yoshimasa Tsuruoka;S. Ananiadou;Junichi Tsujii
提出了一种基于机器学习的缩略词生成方法。我们将生成过程形式化为定义（扩展形式）中字母的序列标签问题，以便各种马尔可夫建模方法可以应用于此任务。为了构建用于训练和测试的数据，我们从MEDLINE摘要中提取了首字母缩略词定义对，并使用首字母缩略词中字母的位置信息手动注释了每个对。我们已经建立了一个基于MEMS的标签使用这个训练数据集，并评估了性能的缩写词生成。实验结果表明，我们的机器学习方法提供了显着更好的性能比标准的启发式规则的缩略词生成，使我们能够获得多个候选缩略词连同他们的概率值表示的可能性。
This paper presents a machine learning approach to acronym generation. We formalize the generation process as a sequence labeling problem on the letters in the definition (expanded form) so that a variety of Markov modeling approaches can be applied to this task. To construct the data for training and testing, we extracted acronym-definition pairs from MEDLINE abstracts and manually annotated each pair with positional information about the letters in the acronym. We have built an MEMM-based tagger using this training data set and evaluated the performance of acronym generation. Experimental results show that our machine learning method gives significantly better performance than that achieved by the standard heuristic rule for acronym generation and enables us to obtain multiple candidate acronyms together with their likelihoods represented in probability values.