对语义文本分类的量子式方法
Anastasia S Gruzdeva1, Rodion N Iurev2, Igor A Bessmertny2
1National Center for Cognitive Research, National Research University for Information Technology, Mechanics and Optics (ITMO), St. Petersburg 197101, Russia.
这项研究引入了用于情感分析的量子波形模型,比古典方法提高了15%的文本分类准确性. 这种方法为分析文本数据提供了对机器学习 (ML) 的计算效率高的替代方案.
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
- 量子启发的计算方式
背景情况:
- 传统的机器学习 (ML) 方法在文本分类和分析方面面临着挑战.
- 现有的模型往往忽略了语言中的复杂语义关系.
- 需要替代文本表示模型来捕捉细微的语言结构.
研究的目的:
- 探索量子式 (基于波的) 模型作为情感分析的ML的替代方案.
- 调查语义干扰对文本分类准确性的影响.
- 开发基于波的文本表示的计算效率高的算法.
主要方法:
- 使用量子波浪模型对英语评论的情感分析.
- 探索受语言结构影响的文本细分算法.
- 量子类模型结果与经典概率方法的比较.
- 开发优化技术以减少计算复杂性.
主要成果:
- 与经典方法相比,量子类型模型将分类准确度提高了约15%.
- 该模型在分类任务中获得了0.8左右的精度和回忆得分.
- 一个提议的优化减少了算法的计算复杂性从O{\displaystyle O} n^2到O{\displaystyle O} n).
结论:
- 量子波浪模型是一种可行的替代方案或补充,用于文本分析的传统ML方法.
- 考虑到量子式的语义干扰,可以提高分类的准确性.
- 开发的模型提供了显著的计算效率改进.
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