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Resolving abbreviations to their senses in Medline

机译:在Medline中解决他们感官的缩写

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Motivation: Biological literature contains many abbreviations with one particular sense in each document. However, most abbreviations do not have a unique sense across the literature. Furthermore, many documents do not contain the long forms of the abbreviations. Resolving an abbreviation in a document consists of retrieving its sense in use. Abbreviation resolution improves accuracy of document retrieval engines and of information extraction systems.Results: We combine an automatic analysis of Medline abstracts and linguistic methods to build a dictionary of abbreviation/sense pairs. The dictionary is used for the resolution of abbreviations occurring with their long forms. Ambiguous global abbreviations are resolved using support vector machines that have been trained on the context of each instance of the abbreviation/sense pairs, previously extracted for the dictionary set-up. The system disambiguates abbreviations with a precision of 98.9% for a recall of 98.2% (98.5% accuracy). This performance is superior in comparison with previously reported research work.
机译:动机:生物文献在每种文献中都包含许多具有特定意义的缩写。但是,大多数缩写在整个文献中都没有独特的含义。此外,许多文件没有包含缩写形式。解决文档中的缩写包括检索其使用意义。缩写解析提高了文档检索引擎和信息提取系统的准确性。结果:我们结合了对Medline摘要和语言方法的自动分析,以构建缩写/义对字典。该词典用于解析以其长格式出现的缩写。使用支持向量机解析歧义的全局缩写,该支持向量机已根据先前为词典设置而提取的每个缩写/义对实例的上下文进行了训练。系统以98.9%的精度消除了缩写词的歧义,召回率为98.2%(准确性为98.5%)。与以前报道的研究工作相比,该性能更好。

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