一种基于语料库的新计算方法来处理关键词排名问题:COVID-19研究文章的一个例子
Liang-Ching Chen1,2, Kuei-Hu Chang3,4
1Department of Foreign Languages R.O.C. Military Academy Kaohsiung Taiwan.
概括
这项研究引入了一种使用希尔什指数 (H-index) 的新型语料库方法,以改善医学文本中的词汇排名,增强对COVID-19等疾病的信息检索能力.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 数据科学是数据科学.
背景情况:
- 在分析文本数据方面,corpora对于AI和机器学习至关重要.
- 在COVID-19等新型疾病爆发期间,从医疗机构有效地检索信息至关重要.
- 传统的基于频率的单词排名方法可能有偏见,不考虑单词频率分散或度.
研究的目的:
- 开发一种基于库的新方法,用于医疗文本中准确的词汇排名.
- 解决传统基于频率的方法在处理词汇排名问题上的局限性.
- 提高医务人员处理新疾病的信息获取效率.
主要方法:
- 开发了一种基于集体的新方法,集成了集体软件和希尔什指数 (H-index) 算法.
- 编制了100篇COVID-19研究文章的集体.
- 将拟议的方法与两种传统的基于频率的方法进行比较.
主要成果:
- 拟议的方法改进了语料库,以获得更准确的词汇排名.
- 该方法同时计算单词的频率分散和度标准.
- 与传统的基于频率的方法相比,结果显示出更高的准确性.
结论:
- 基于Hirsch索引的新型语料库方法为医学文本中的词汇排名提供了更准确的方法.
- 这种方法提高了从专业公司获取信息的效率.
- 它为医疗专业人员在健康危机期间导航关键信息提供了宝贵的工具.
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