一项关于英语风格和形式特征的分类的研究,基于语料库数据测试
1School of Foreign Studies, South China Agricultural University, Guangzhou, Guangdong, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种改进的算法,用于中文单词细分,增强新单词的识别. 新方法提高了准确性和回忆力,改善了语言信息服务.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 像N-gram这样的传统单词分割算法在处理单词凝聚力和效率方面存在局限性.
- 现有的方法难以准确识别新词,导致实验效率低下.
研究的目的:
- 开发一种改进的算法,用于中文单词细分,并增强新单词识别.
- 提高处理中文语言数据的准确性和效率.
主要方法:
- 一个中国新词汇集的构建.
- 采用改进的多点互联信息 (多PMI) 应用,新词过的门是双重的.
- 使用分支来计算单词概率.
- 集体的预处理,然后进行N-gram算法细分.
主要成果:
- 与传统方法相比,拟议的算法在准确性 (7%),回忆 (3%) 和F测量 (5%) 方面取得了改进.
- 通过多个单词的相互信息和双值来提高新单词的识别精度.
结论:
- 开发的算法提供了一种更有效的方法来对中文单词细分和新单词识别.
- 这一进步促进了更好的语言资源共享,并改善了对在线语言信息服务的访问.
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