Spatial distributional semantic models represent word meanings in a vector space. While able to model many basic semantic tasks, they are limited in many ways, such as their inability to represent multiple kinds of relations in a single semantic space and to directly leverage indirect relations between two lexical representations. To address these limitations, we propose a distributional graphical model that encodes lexical distributional data in a graphical structure and uses spreading activation for determining the plausibility of word sequences. We compare our model to existing spatial and graphical models by systematically varying parameters that contributing to dimensions of theoretical interest in semantic modeling. In order to be certain about what the models should be able to learn, we trained each model on an artificial corpus describing events in an artificial world simulation containing experimentally controlled verb-noun selectional preferences. The task used for model evaluation requires recovering observed selectional preferences and inferring semantically plausible but never observed verb-noun pairs. We show that the distributional graphical model performed better than all other models. Further, we argue that the relative success of this model comes from its improved ability to access the different orders of spatial representations with the spreading activation on the graph, enabling the model to infer the plausibility of noun-verb pairs unobserved in the training data. The model integrates classical ideas of representing semantic knowledge in a graph with spreading activation and more recent trends focused on the extraction of lexical distributional data from large natural language corpora. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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分布式语义知识的空间表示与图形表示。


空间分布语义模型表示向量空间中的单词含义。虽然能够对许多基本语义任务进行建模，但它们在许多方面受到限制，例如它们无法在单个语义空间中表示多种关系，也无法直接利用两个词汇表示之间的间接关系。为了解决这些限制，我们提出了一种分布式图形模型，该模型以图形结构对词汇分布数据进行编码，并使用扩散激活来确定单词序列的合理性。我们通过系统地改变有助于语义建模理论兴趣维度的参数，将我们的模型与现有的空间和图形模型进行比较。为了确定模型应该能够学习什么，我们在一个人工语料库上训练了每个模型，该语料库描述了人工世界模拟中的事件，其中包含实验控制的动词-名词选择偏好。用于模型评估的任务需要恢复观察到的选择偏好并推断语义上合理但从未观察到的动词-名词对。我们表明，分布式图形模型的性能优于所有其他模型。此外，我们认为该模型的相对成功来自于它通过图上的扩散激活访问不同顺序的空间表示的能力的提高，使模型能够推断训练数据中未观察到的名词-动词对的合理性。该模型融合了在图中表示语义知识的经典思想与传播激活以及关注从大型自然语言语料库中提取词汇分布数据的最新趋势。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。