确定最佳的环境信息,用于训练词汇语义和词汇组织的计算模型
1Department of Psychology, McGill University.
概括
这项研究通过比较基于用户和基于话语的培训材料来优化认知模型. 经验优化揭示了哪些体最能增强词汇语义和组织模型.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 心理学 心理学 心理学
背景情况:
- 经验理论认为外部环境塑造认知,强调学习环境结构.
- 词汇语义和组织的计算模型证明了语言体验对认知表征的影响.
- 对这些模型的最佳培训材料仍未得到充分探索,尽管最近对话语和以用户为中心的文本进行了研究.
研究的目的:
- 确定词汇语义和组织的计算模型的最佳培训材料.
- 为了比较基于用户的与基于话语的 corpora 在优化这些模型的有效性.
- 通过数据驱动优化,提供关于整合认知模型的见解.
主要方法:
- 利用经验优化 (Johns, Jones, & Mewhort,2019) 来选择最大化模型性能的材料.
- 作为训练数据集,比较基于用户的和基于话语的 corpora.
- 适用于词汇组织和词汇语义模型的优化.
主要成果:
- 确定了在词汇组织模型中显著提高性能的特定体.
- 对于词汇语义模型来说,产生最佳结果的确定体.
- 证明了体验优化在选择培训数据中的有效性.
结论:
- 训练corpora的选择对认知模型的性能产生了重大影响.
- 基于用户和基于话语的材料为不同的模型类型提供了明显的优势.
- 经验优化为推进计算认知科学提供了一个强大的框架.
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