使用术语识别方法构建缩写词典。
Building an abbreviation dictionary using a term recognition approach.
作者信息
Okazaki Naoaki, Ananiadou Sophia
机构信息
Graduate School of Information Science and Technology, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8651, Japan.
出版信息
Bioinformatics. 2006 Dec 15;22(24):3089-95. doi: 10.1093/bioinformatics/btl534. Epub 2006 Oct 18.
MOTIVATION
Acronyms result from a highly productive type of term variation and trigger the need for an acronym dictionary to establish associations between acronyms and their expanded forms.
RESULTS
We propose a novel method for recognizing acronym definitions in a text collection. Assuming a word sequence co-occurring frequently with a parenthetical expression to be a potential expanded form, our method identifies acronym definitions in a similar manner to the statistical term recognition task. Applied to the whole MEDLINE (7 811 582 abstracts), the implemented system extracted 886 755 acronym candidates and recognized 300 954 expanded forms in reasonable time. Our method outperformed base-line systems, achieving 99% precision and 82-95% recall on our evaluation corpus that roughly emulates the whole MEDLINE.
AVAILABILITY AND SUPPLEMENTARY INFORMATION
The implementations and supplementary information are available at our web site: http://www.chokkan.org/research/acromine/
动机
首字母缩略词是一种高效的术语变体类型，这引发了对首字母缩略词词典的需求，以便在首字母缩略词与其扩展形式之间建立关联。
结果
我们提出了一种在文本集合中识别首字母缩略词定义的新方法。假设经常与括号表达式同时出现的单词序列为潜在的扩展形式，我们的方法以类似于统计术语识别任务的方式识别首字母缩略词定义。应用于整个MEDLINE（7811582篇摘要），该实现系统在合理时间内提取了886755个首字母缩略词候选词，并识别出300954个扩展形式。我们的方法优于基线系统，在大致模拟整个MEDLINE的评估语料库上达到了99%的精确率和82 - 95%的召回率。
可用性和补充信息
实现方法和补充信息可在我们的网站获取：http://www.chokkan.org/research/acromine/
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