感知相似性作为一个窗口,用于表示集成句子的含义
Sophie Arana1, Peter Hagoort2,3, Jan-Mathijs Schoffelen3
1Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands. sophie.arana@psy.ox.ac.uk.
Behavior research methods
|June 29, 2023
概括
这项研究通过分析感知相似性来量化句子的含义. 句子相似性主要是由动词语义驱动的,揭示了潜在的关系信息,这对认知和人工智能模型有价值.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 心理语言学 心理语言学
背景情况:
- 人类的感知整合了超越单个组件的信息,这在场景和句子理解中很明显.
- 综合表示的定量模型对于评估语言和感知认知理论至关重要.
- 感知到的句子相似性作为一个行为代理集成的含义表示.
研究的目的:
- 利用行为相似性判断,开发一个整合句子意义的定量模型.
- 调查调整句子之间感知相似性的因素.
- 将人类相似性判断与人工神经网络 (ANN) 的表示进行比较.
主要方法:
- 通过在线多重排列任务,收集了200名受试者对名词和过渡性句子的相似性判断.
- 应用非负矩阵分解来分析收集的相似性数据.
- 从三个最先进的ANN中提取句子相似性进行比较.
主要成果:
- 感知到的句子相似性主要受到主要动词的语义行动类别的影响.
- 非负矩阵因子化揭示了捕获语义和关系角色信息的维度.
- 行为数据为评估ANN捕获句子级关系信息的能力提供了基准.
结论:
- 多重排列任务与矩阵因子化相结合,有效地捕获句子含义中的关系信息.
- 动词语义在人类如何感知句子相似性方面发挥着至关重要的作用.
- 这种方法为在语言处理中将人类认知与ANN进行比较提供了一个有价值的方法.
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