Article published in:
Construction Grammar across Borders
Edited by Tiago Timponi Torrent, Ely Edison da Silva Matos and Natália Sathler Sigiliano
[Constructions and Frames 12:1] 2020
► pp. 149–169

Advances in Embodied Construction Grammar

| International Computer Science Institute | University of California Berkeley
This paper describes the continuing goals and present status of the ICSI/UC Berkeley efforts on Embodied Construction Grammar (ECG). ECG is semantics-based formalism grounded in cognitive linguistics. ECG is the most explicitly inter-disciplinary of the construction grammars with deep links to computation, neuroscience, and cognitive science. Work continues on core cognitive, computational, and linguistic issues, including aspects of the mind/body problem. Much of the recent emphasis has been on applications and on tools to facilitate new applications. Extensive documentation plus downloadable systems and grammars can be found at the ECG Homepage.
Keywords: embodiment, semantics, best fit, construction, compositionality, robotics, workbench, framework
Published online: 29 July 2020
https://doi.org/10.1075/cf.00038.fel
References

References

Bergen, B.
(2014) Louder than words: The new science of how the mind makes meaning. New York: Basic Books.Google Scholar
Bergen, B., Chang, N., & Narayan, S.
(2004) Simulated action in an embodied construction grammar. In Proceedings of the 26th Annual Meeting of the Cognitive Science Society, 26, 108–113.Google Scholar
Bryant, J.
(2008) Best fit constructional analysis. Unpublished UC Berkeley dissertation.Google Scholar
Chang, N., & Mok, E.
(2006) Putting context in constructions. In Proceedings of the 4th International Conference on Construction Grammar (ICCG4). Tokyo, Japan.Google Scholar
C&F (2013) In memory of Charles J. Fillmore. Constructions and Frames, 5(2), 117–118. CrossrefGoogle Scholar
Dodge, E., David, O., Stickles, E., & Sweetser, E.
(2014)  Constructions and metaphor: Integrating MetaNet and embodied construction grammar. Paper presented at The 8th International Construction Grammar Conference.Google Scholar
Dodge, E.
(2016) A deep semantic corpus-based approach to metaphor analysis: A case study of metaphoric conceptualizations of poverty. Constructions and Frames, 8(2), 256–294. CrossrefGoogle Scholar
Dodge, E., Trott, S., Gilardi, L., & Stickles, E.
(2017) Grammar scaling: Leveraging FrameNet data to increase embodied construction grammar coverage. The AAAI 2017 Spring Symposium on Computational Construction Grammar and Natural Language Understanding. Technical Report SS-17–02 (pp. 154–162). Palo Alto, CA: AAAI Publications.Google Scholar
Eppe, M., Trott, S., Raghuram, V., Feldman, J., & Janin, A.
(2016) Application-independent and integration-friendly natural language understanding. Global Conference on Artificial Intelligence (GCAI 2016) (pp. 340–352). Berlin: EasyChair, EPiC Series in Computing, v.41.Google Scholar
Eppe, M., Trott, S., & Feldman, J.
(2016a) Exploiting deep semantics and compositionality of natural language for human-robot interaction. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 731–738). Daejeon, South Korea: IEEE. CrossrefGoogle Scholar
Feldman, J.
(2006) From molecule to metaphor: A neural theory of language. MIT Press. CrossrefGoogle Scholar
(2010) Embodied language, best-fit analysis, and formal compositionality. Physics of Life Reviews, 7(4), 385–410. CrossrefGoogle Scholar
(2017) Mysteries of visual experience. http://​arxiv​.org​/abs​/1604​.08612
(2018) Towards a science of the mind. https://​arxiv​.org​/abs​/1811​.06825
Feldman, J., Lakoff, G., Bailey, D., Narayanan, S., Regier, T., & Stolcke, A.
(1996) L0 – the first five years of an automated language acquisition project. In P. Mc Kevitt (Ed.), Integration of Natural Language and Vision Processing: Theory and Grounding Representations Volume III (pp. 205–231). Dordrecht: Springer Netherlands. CrossrefGoogle Scholar
Feldman, J., Bryant, J., & Dodge, E.
(2009) A neural theory of language and embodied construction grammar. The Oxford Handbook of Computational Linguistics (pp. 38–111). Oxford: Oxford University Press.Google Scholar
Fillmore, C., Johnson, C. R., & Petruck, M.
(2003) Background to Framenet. International Journal of Lexicography, 16(3), 235–250. CrossrefGoogle Scholar
Gedigian, M., & Narayanan, S.
(2009) A multilingual primary health care resource. Proceedings of the Computer Science and Global Development Conference (pp. 63–65). Washington, DC: Computing Community Consortium.Google Scholar
Goldberg, A.
(1995) Constructions: A construction grammar approach to argument structure. University of Chicago Press.Google Scholar
Khayrallah, H., Trott, S., & Feldman, J.
(2015) Natural language for human-robot interaction. Paper presented at the International Conference on Human-Robot Interaction (HRI), Portland, Oregon.Google Scholar
Lakoff, G. & Johnson, M.
(1980) Metaphors we live by. Chicago: University of Chicago Press.Google Scholar
Lakoff, G.
(1987) Women, fire, and dangerous things: What categories reveal about the mind. Chicago: University of Chicago Press. CrossrefGoogle Scholar
Levesque, H., Davis, E., & Morgenstern, L.
(2012) The Winograd schema challenge. In Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning (pp. 552–561). Palo Alto, CA: AAAI Press.Google Scholar
Michaelis, L. A.
(2017) Construction grammar and the syntax-semantics interface. Bloomsbury Companion to Syntax (pp. 421–435). New York: Bloomsbury.Google Scholar
Mok, E. H., & Bryant, J.
(2006) A best-fit approach to productive omission of arguments. Annual Meeting of the Berkeley Linguistics Society, 32 (1), 551–560. CrossrefGoogle Scholar
Narayanan, S.
(1999a) Reasoning about actions in narrative understanding. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI ’99) (pp. 350–358). San Francisco: Morgan Kaufmann.Google Scholar
(1999b) Moving right along: A computational model of metaphoric reasoning about events. In Proceedings of the Nat. Conf. on Artificial Intelligence (AAAI ’99) (pp. 121–128). Palo Alto, CA: AAAI Press.Google Scholar
Narayanan, S., & Jurafsky, D.
(1998) Bayesian models of human sentence processing. In Proceedings of the 20th Annual Conference of the Cognitive Science Society (pp. 752–757). Allendale, NJ: Erlbaum.Google Scholar
Newell, A.
(1994) Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Petruck, M.
(Ed.) (2016) Constructions and Frames [Special issue on Metanet], 8(2). CrossrefGoogle Scholar
Raghuram, V., Trott, S., Shen, K., Goldberg, E., & Oderberg, S.
(2017) Semantically-driven coreference resolution with embodied construction grammar. In Proceedings of the AAAI 2017 Spring Symposium on Computational Construction Grammar and Natural Language Understanding. Technical Report SS-17–02 (pp. 238–244). Palo Alto, CA: AAAI Publications.Google Scholar
Raghuram, V.
(2018) Natural language understanding for healthcare queries. UC Berkeley Technical Report No. UCB/EECS-2018-35. http://​www2​.eecs​.berkeley​.edu​/Pubs​/TechRpts​/2018​/EECS​-2018​-35​.pdf
Regier, T.
(2002) The human semantic potential. Cambridge, MA: MIT Press.Google Scholar
Stickles, E., David, O., & Sweetser, E.
(2015) Grammatical constructions, frame structure, and metonymy: Their contributions to metaphor computation. In Proceedings of the 11th Meeting of the High Desert Linguistics Society (HDLS) (pp. 317–345). Albuquerque, NM: High Desert Linguistics Society.Google Scholar
Trott, S., Eppe, M., & Feldman, J.
(2016) Recognizing intentions from natural language: Clarification dialog and construction grammar. Paper presented at RO-MAN 2016, Workshop on Communicating Intentions in Human-Robot Interaction . New York, NY.