BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature.
BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature.
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DOI：
10.1186/1471-2105-10-s15-s7
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发表时间：
2009-12-03
影响因子：
3
通讯作者：
Hsu CN
中科院分区：
文献类型：
--
作者：
Kuo CJ;Ling MH;Lin KT;Hsu CN
为了自动处理大量的生物文献以进行知识发现和信息管理，文本挖掘工具变得至关重要。缩略语识别与名词短语识别有关，可以看作是从自由文本中识别术语及其对应缩略语的一项配对任务。缩略语及其对应定义的成功识别不仅是对文本数据库中的术语进行索引以产生相关文章的前提，也是改进现有的基因提及标注和基因标准化工具的基础。我们的缩写识别方法是基于机器学习的，它利用一组新颖的丰富特征来从训练数据中学习规则。在AB3P语料库上进行测试，系统的F-Score为89.90%，准确率为95.86%，召回率为84.64%，高于现有最佳AR性能系统的测试结果。我们还对来自BioCreative II基因归一化语料库的1200篇PubMed摘要进行了标注。在我们标注的语料库上，我们的系统获得了86.20%的F-Score和93.52%的准确率，召回率为79.95%，也超过了所有测试系统的性能。通过应用我们的系统从所有可用的PubMed摘要中提取所有短形式-长形式对，我们构造了BIOADI。挖掘BIOADI揭示了生物医学研究的许多有趣趋势。此外，我们还在http://bioagent.iis.sinica.edu.tw/BIOADI/.上的下载部分提供了离线AR软件
To automatically process large quantities of biological literature for knowledge discovery and information curation, text mining tools are becoming essential. Abbreviation recognition is related to NER and can be considered as a pair recognition task of a terminology and its corresponding abbreviation from free text. The successful identification of abbreviation and its corresponding definition is not only a prerequisite to index terms of text databases to produce articles of related interests, but also a building block to improve existing gene mention tagging and gene normalization tools. Our approach to abbreviation recognition (AR) is based on machine-learning, which exploits a novel set of rich features to learn rules from training data. Tested on the AB3P corpus, our system demonstrated a F-score of 89.90% with 95.86% precision at 84.64% recall, higher than the result achieved by the existing best AR performance system. We also annotated a new corpus of 1200 PubMed abstracts which was derived from BioCreative II gene normalization corpus. On our annotated corpus, our system achieved a F-score of 86.20% with 93.52% precision at 79.95% recall, which also outperforms all tested systems. By applying our system to extract all short form-long form pairs from all available PubMed abstracts, we have constructed BIOADI. Mining BIOADI reveals many interesting trends of bio-medical research. Besides, we also provide an off-line AR software in the download section on http://bioagent.iis.sinica.edu.tw/BIOADI/.