Abstract
This paper puts forward a finer-grained computational treatment of the English caused-motion construction (e.g. He kicked the ball into the net) within a knowledge base for natural language processing systems called FunGramKB. This computational project is largely based on Role and Reference Grammar (RRG), which is a functional projectionist theory of language. We argue that the RRG-based characterization of the caused-motion construction in FunGramKB is insufficient to account for the semantic and syntactic complexity of realizations such as He walked the dog to the park, I will show you out, or Mac flew Continental to Bush International Airport. Thus, drawing on insights from Constructions Grammars, three minimally distinct transitive motion sub-constructions are formalized within FunGramKB. It is through the inclusion of additional constructional schemas that the machine will be able to capture the various ways in which verbs and constructions interact to yield different input texts.
Highlights
Several scholars have emphasized the numerous ways in which theoretical linguistics can enhance Natural Language Processing (NLP), and the need to bridge the gap between these areas of research
Contrary to Van Valin and Mairal’s (2014) claim, we argue that the present formalization of the caused-motion constructional schema in FunGramKB does not do justice to the actual complexity of this configuration
Having explained the way verbs are handled in the Lexicon, as well as the current computational treatment of the caused-motion construction, we show how the NLP system would generate the semantic representation of the caused-motion realization in (1a) through a conceptual logical structure’ (CLS): (1) (a) Input text: Freddie broke the walnuts into the bowl
Summary
This paper has shown that the current constructional schema of the caused-motion or causative spatial resultative construction is too broad to account for the semantic and syntactic complexity of possible realizations of the same general construction. In order to provide a finer-grained computational treatment, we have drawn insights from CxG approaches in an attempt to enhance the current RRG-based constructional schemas in FunGramKB. This procedure is consistent with the very nature of FunGramKB, a computational project that bridges the gap between theoretical linguistics and NLP. On the basis of two cognitive operations, we can account for diverse realizational possibilities without the need to posit very specific constructions, the highly abstract nature of such cognitive mechanisms does not comply with computational requirements
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