Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models

This paper presents a large-scale evaluation of bag-of-words distributional models on two datasets from priming experiments involving syntagmatic and paradigmatic relations. We interpret the variation in performance achieved by different settings of the model parameters as an indication of which aspects of distributional patterns characterize these types of relations. Contrary to what has been argued in the literature (Rapp, 2002; Sahlgren, 2006) – that bag-of-words models based on secondorder statistics mainly capture paradigmatic relations and that syntagmatic relations need to be gathered from first-order models – we show that second-order models perform well on both paradigmatic and syntagmatic relations if their parameters are properly tuned. In particular, our results show that size of the context window and dimensionality reduction play a key role in differentiating DSM performance on paradigmatic vs. syntagmatic relations.