Metaphors of emotions: towards a data-driven formalization

Authors

DOI:

https://doi.org/10.32589/2311-0821.24%20(1).2021.236030

Keywords:

artificial intelligence, cognitive linguistics, formal representation of metaphors, human emotion, intensional definition, ontology, schema

Abstract

Formalization of natural language metaphors is a notorious problem in artificial intelligence and in other overlapping domains. It is semantic vagueness that makes metaphors resistant to formulaic or algorithmic descriptions. Great effort has been invested into modeling metaphors computationally but the issue remains methodologically uncertain and needs further research. This paper works on a practical solution to the problem how metaphorical meaning can be represented in a way suitable for computation. The research agenda of this paper is interdisciplinary; it brings together an algebraic heuristic-driven theory for metaphors developed in artificial intelligence and an applied theory of meaning that comes from cognitive linguistics. This agenda postpones theoretical speculation and argument and is solely solution-focused, which contributes to the value of this paper’s attempt to bridge the cognitive science disciplines whose compatibility, though declared, is seldom
demonstrated in a piece of practical research.
This paper works with metaphors of human emotions that are linguistically manifested in modern English discourse. Emotions by virtue of their ineffability as qualia are rich in metaphorical conceptualizations and serve the research agenda well. This paper in a meaningful way exposes and ranks designated properties of the FEAR, SADNESS, HAPPINESS, and RELAXATION/SERENITY concepts and arranges these properties into general-purpose ontologies that explicitly specify metaphorically preferred emotion conceptualizations and are good candidates for computation. In prospect, this paper will account for some theoretical aspects of the research and probe the algorithmic and repetitive nature of schemas that license metaphorical expressions in natural language.

References

Жаботинська, С. (2019). Семантика лінгвальних мереж у навчальному комбінаторному тезаурусі. Studia Philologica, 13, 17-27. doi: 10.28925/2311-2425.2019.13.3

Никитин, М. В. (2007). Курс лингвистической семантики. Санкт Петербург: РГПУ им. А. И. Герцена.

Besold, T. R., Kühnberger, K.-U., & Plaza, E. (2017). Towards a computational- and algorithmic-level account of concept blending using analogies and amalgams. Connection Science, 29(4), 387-413. doi: 10.1080/09540091.2017.1326463

Eppe, M., Maclean, E., Confalonieri, R., Kutz, O., Schorlemmer, M., Plaza, E., & Kühnberger, K.-U. (2018). Acomputational framework for conceptual blending. Artificial Intelligence, 256, 105-129. doi: 10.1016/j.artint.2017.11.005

Gentner, D., & Forbus, K.D. (2011). Computational models of analogy. Cognitive Science, 2(3), 266-276.

Gust, H., Kühnberger, K.-U., & Schmid, U. (2004). Ontological aspects of computing analogies. In Proceedings of the Sixth International Conference on Cognitive Modeling (pp. 350-351). Mahwah, NJ: Lawrence Earlbaum.

Gust, H., Kühnberger, K.-U., & Schmid, U. (2006). Metaphors and heuristic-driven theory projection (HDTP). Theoretical Computer Science, 354(1), 98-117.

Gust, H., Kühnberger, K.-U., & Schmid, U. (2007). Ontologies as a cue for the metaphorical meaning of technical concepts. InA. Schalley and D. Khlentzos (Eds.), Mental States: Evolution, Function, Nature (pp. 191-212). Amsterdam/Philadelphia: Benjamins.

Kemmer, S. (2003). Schemas and lexical blends. In H. Cuyckens, T. Berg, R. Dirven † and Klaus- Uwe Panther (Eds.), Motivation in Language: Studies in Honour of Gunter Radden (pp. 69-97). John Benjamins Publishing Company. doi: 10.1075/cilt.243.08kem

Kövecses, Z. (2017). Levels of metaphor. Cognitive Linguistics, 28(2), 321-347.

Peters, S., & Shrobe, H. E. (2003). Using semantic networks for knowledge representation in an intelligent environment. IEEE International Conference on Pervasive Computing and Communications (PerCom’03), 1, 323-337. doi: 10.1109/PERCOM.2003.1192756

Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695-729.

Schmid, U., Gust, H., Kühnberger, K.-U., & Burghardt, J. (2003). An algebraic framework for solving proportional and predictive analogies. The European Cognitive Science Conference. [eBook edition]. doi: 10.4324/9781315782362-59.

Vakhovska, O. (2017a). A cognitive linguistic perspective on first-person verbal report on emotion experience. Вісник Харківського національного університету імені В. Н. Каразіна. Серія: Іноземна філологія. Методика викладання іноземних мов, 85, 72-80.

Vakhovska, O. (2017b). Metaphor in the light of conceptual metaphor theory: A literature review. Cognition, Communication, Discourse, 15, 84-103. doi: 10.26565/2218-2926-2017-15-06.

Downloads

Published

2021-07-06

Issue

Section

Articles