Representing Caused Motion in Embodied Construction Grammar

Abstract

This paper offers an Embodied Construction Grammar (Feldman et al. 2010) representation of caused motion, thereby also providing (a sample of) the computational infrastructure for implementing the information that FrameNet has characterized as Caused_motion 1 (Ruppenhofer et al. 2010). This work specifies the semantic structure of caused motion in natural language, using an Embodied Construction Grammar analyzer that includes the semantic parsing of linguistically instantiated constructions. Results from this type of analysis can serve as the input to NLP applications that require rich semantic representations.

Constructionist approaches: Past, present, future

“…speakers' tendency to activate perceptual and motor systems in the brain that recreate experiences similar to the ones that arise during actual perception or movement (Barsalou 1999). Previous studies have used Embodied CxG to analyze phenomena such as the English caused motion construction (Dodge & Petruck 2014) and Hebrew verbal morphology (Schneider 2010), and to model aspects of grammatical parsing (Bryant 2008) and acquisition (Mok 2009). Recent work, meanwhile, has somewhat moved away from linguistic analysis and instead focused on technological applications in natural language understanding, including verbal control of robots (Eppe et al 2016) and a system for providing health advice (Feldman 2020).…”

Constructionist Approaches

“…As Bergen and Chang (2013) outline, the framework aims to account for the role of embodied simulation in language processing, that is, speakers' tendency to activate perceptual and motor systems in the brain that recreate experiences similar to the ones that arise during actual perception or movement (Barsalou 1999). Previous studies have used Embodied CxG to analyze phenomena such as the English caused-motion construction (Dodge & Petruck 2014) and Hebrew verbal morphology (Schneider 2010), and to model aspects of grammatical parsing (Bryant 2008) and acquisition (Mok 2009). Recent work, meanwhile, has somewhat moved away from linguistic analysis and instead focused on technological applications in natural language understanding, including verbal control of robots (Eppe et al 2016) and a system for providing health advice .…”

(Re)construing Meaning in NLP

“…5 There is extensive psycholinguistic evidence of constructional coercion and the many factors influencing ease of construal (see Goldberg (2003Goldberg ( , 2019 for reviews). Some of these phenomena have been analyzed within computational implementations of construction grammar (Bergen and Chang, 2005;Bryant, 2008;Bergen and Chang, 2013;Dodge and Petruck, 2014;Steels, 2017;Steels and Feldman, 2017;Matos et al, 2017), and have also been incorporated in corpus annotation schemes (Bonial et al, 2011;Hwang et al, 2014;Lyngfelt et al, 2018).…”