Previous research has demonstrated that Distributional Semantic Models (DSMs) are capable of reconstructing maps from news corpora (Louwerse & Zwaan, 2009) and novels (Louwerse & Benesh, 2012). The capacity for reproducing maps is surprising since DSMs notoriously lack perceptual grounding. In this paper we investigate the statistical sources required in language to infer maps, and the resulting constraints placed on mechanisms of semantic representation. Study 1 brings word co-occurrence under experimental control to demonstrate that standard DSMs cannot reproduce maps when word co-occurrence is uniform. Specifically, standard DSMs require that direct co-occurrences between city names in a corpus mirror the proximity between the city locations in the map in order to successfully reconstruct the spatial map. Study 2 presents an instance-based DSM that is capable of reconstructing maps independent of the frequency of co-occurrence of city names.

先前的研究表明，分布式语义模型 (DSM) 能够从新闻语料库 (Louwerse & Zwaan, 2009) 和小说 (Louwerse & Benesh, 2012) 中重建地图。复制地图的能力令人惊讶，因为众所周知，DSM 缺乏感知基础。在本文中，我们研究了语言中推断地图所需的统计来源，以及由此产生的对语义表示机制的约束。研究 1 将词共现置于实验控制之下，以证明当词共现统一时，标准 DSM 无法再现地图。具体来说，标准 DSM 要求语料库中城市名称之间的直接共现反映地图中城市位置之间的接近程度，以便成功重建空间地图。