一个词的地图:通过分布式语义检索中等规模的地理地图的空间布局
Giorgia Anceresi1, Daniele Gatti1, Tomaso Vecchi2
1Department of Brain and Behavioral Sciences, University of Pavia, Piazza Botta 6, 27100, Pavia, Italy.
Neuropsychologia
|June 4, 2025
概括
空间信息,如城市地图,可以使用分布式语义模型从自然语言中提取. 这项研究表明,语言数据可以表示中等规模的空间细节,挑战了对认知地图形成的传统观点.
科学领域:
- 认知科学 认知科学
- 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:
- 传统上,人们认为空间表示依赖于专门的空间计算.
- 最近的工作表明,分布式语义模型可以从文本中推导出大规模的空间地图.
- 从语言中提取中等规模的空间信息的研究仍然较少.
研究的目的:
- 调查是否可以从自然语言中提取中等规模的空间信息 (例如,城市地标).
- 为了确定不同类型的空间表示 (相对距离与绝对坐标) 是否可以检索.
- 评估语言数据与城市交通系统视觉地图的一致性.
主要方法:
- 利用在大型语言群体上训练的认知可信的分布式语义模型.
- 应用模型从五个欧洲城市的地下地图的自然语言描述中提取空间信息.
- 将提取的空间表示与地理和图形视觉地图进行比较,重点是伦敦地铁.
主要成果:
- 从五个欧洲城市的纯语言数据中成功检索空间表示,包括相对距离和绝对坐标.
- 展示了语言数据有效地与伦敦地铁的地理和图形视觉地图保持一致.
- 确认从自然语言中提取了中等规模的空间信息.
结论:
- 自然语言包含中等规模的可提取空间信息,而不仅仅是大规模的地理数据.
- 在语言环境中的非空间关联学习机制在形成空间表征方面发挥着至关重要的作用.
- 挑战了用于认知地图形成的专门空间计算的必要性,强调了语言的贡献.
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