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在线性歧视性学习中的频率效应.

Maria Heitmeier1,2, Yu-Ying Chuang1, Seth D Axen2

  • 1Quantitative Linguistics, University of Tübingen, Tübingen, Germany.

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概括
此摘要是机器生成的。

本研究介绍了基于频率的学习 (FIL),这是一个有效的方法来模拟词汇处理中的单词频率效应. FIL准确地预测了反应时间和原始化效应,为增量学习提供了更便宜的计算替代方案.

关键词:
分布式语义学的分布式语义学.增量学习是一种增量学习.词汇决定的词汇决定线性歧视性学习是一种线性歧视性学习.心理词汇词典 精神词典权重回归的加权回归方法单词的频率 词的频率

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科学领域:

  • 认知科学 认知科学
  • 心理语言学 心理语言学
  • 计算语言学 计算语言学

背景情况:

  • 单词频率显著影响词汇处理和词识别模型.
  • 现有的模型,如歧视性词典模型 (DLM),难以有效地纳入频率效应.
  • 目前的解决方案包括计算上昂贵的增量学习或频率不可知终端状态学习 (EL).

研究的目的:

  • 为词汇处理模型开发一种高效且基于频率的学习方法.
  • 评估这种新方法 (频率知情学习;FIL) 与现有方法的性能.
  • 调查 FIL 如何考虑反应时间和原始化中的频率效应.

主要方法:

  • 开发并实施了频率信息化学习 (FIL) 算法.
  • 模拟反应时间使用荷兰词典项目数据与高斯位置尺度模型.
  • 分析了用普通话中文数据在听觉词汇决策任务中的原始化效应.
  • 使用CHILDES数据,比较了 FIL 映射与增量学习.

主要成果:

  • FIL提供了增量学习的高效近似,大大降低了计算成本.
  • 文件表现出高的标记精度,有效地处理常用单词.
  • FIL准确地预测了S型的频率-反应时间关系,但低估了低频词的差异.
  • FIL比EL更好地解释了原始化效应,并且显示了与增量学习映射的高相关性,尽管一些文字排序细微差别丢失了.

结论:

  • 基于频率的学习 (FIL) 提供了一种有效的方式来模拟词识别认知模型中的频率效应.
  • 对于模拟词汇处理而言,FIL为增量学习提供了一个可行的,在计算上更便宜的替代方案.
  • 需要进一步的研究来完善认知模型中的低频词和词排序效应的建模.