Abstract
An analogy is a relation which operates between two pairs of terms representing two distant domains. It operates by transferring meaning from a concept that is known to another that one would like to clarify or define. In this report, we address analogy both from the aspect of modeling and by automatically explaining it. We will then propose a system of resolution of analogical equations in their notation in symbol chains. The model, based on the common sense knowledge base JeuxDeMots (a semantic network), operates by generating a list of potential candidates from which it chooses the most suitable solution. We conclude by evaluating our model on a collection of equations, and reflecting upon future work.
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Notes
- 1.
- 2.
Some relations are conversive, that is to say that \(a~r\_t~b \Leftrightarrow b~r\_t_{-1}~a\) has with \(r\_t_{-1}\) the conversive relation to \(r\_t\) (example: \(r\_isa\) and \(r\_hypo\)).
- 3.
We have arbitrarily chosen in our demonstrator the 2 most relevant relationships (if there are any) for reasons of simplification. Note that the intermediate node may be the same for these 2 relationships.
- 4.
The word ‘noyau’ in French has a translation of ‘stone’ as in the hard core of stone fruits.
- 5.
Note that the strength of relational similarity from A to B is not necessarily the same as that from B to A since there are relationships oriented in both directions and of different weights.
- 6.
In the future, we will be able to use a TF-IDF type approach which consists of seeing how an ArB is as specific as possible to A, such an approach would however be computationally intensive.
- 7.
Arithmetic and geometric means produce similar results.
- 8.
We have only discussed metaphors so far, comparisons being a broader subject given that their two unknowns in the context of the analogical square constitute a combinatorial challenge.
- 9.
By simulating the intersections for a list of just over 2000 metaphors played in JDM (http://jeuxdemots.org/analogies.php \(\rightarrow \) “exporter données”), we note that around 87% of failure cases are due to missing relationships. Deductive inference processes can overcome this problem in 65% of cases. Example of inference: child r_has_part leg \(\rightarrow \) child r_isa human r_has_part leg.
- 10.
Passage through lemmatization by observing all the relationships of close words such as petit, petite, petits, petites (which means small in feminine and masculine and in singular and plural in french) when one of them is concerned.
- 11.
P being the semantic relation seen as a unary predicate of the same value.
References
Gentner, D.: Structure-mapping: a theoretical framework for analogy. Cognitive science 7(2), 155–170 (1983). https://www.sciencedirect.com/science/article/pii/S0364021383800093, publisher: Elsevier
Gentner, D., Holyoak, K.J.: Reasoning and learning by analogy: Introduction. Am. Psychol. 52(1), 32 (1997). https://psycnet.apa.org/record/1997-02239-004, publisher: American Psychological Association
Hofstadter, D.R.: Fluid concepts and creative analogies: computer models of the fundamental mechanisms of thought. Basic books (1995). https://psycnet.apa.org/record/1995-98269-000
Hofstadter, D.R.: Analogy as the core of cognition. The analogical mind: Perspectives from cognitive science (2001)
Kahneman, D.: Thinking, fast and slow. Farrar, Straus and Giroux (2017)
King, D., Gentner, D.: Verb Metaphors are Processed as Analogies (2023)
Lafourcade, M.: Making people play for Lexical Acquisition with the JeuxDeMots prototype. In: SNLP’07: 7th International Symposium on Natural Language Processing, p. 7 (2007). https://hal-lirmm.ccsd.cnrs.fr/lirmm-00200883/
Lepage, Y.: De l’analogie rendant compte de la commutation en linguistique. Ph.D. thesis, Université Joseph-Fourier-Grenoble I (2003)
Shutova, E.: Automatic metaphor interpretation as a paraphrasing task. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 1029–1037 (2010). https://aclanthology.org/N10-1147.pdf
Turney, P.D.: Measuring Semantic Similarity by Latent Relational Analysis (2005). http://arxiv.org/abs/cs/0508053, arXiv:cs/0508053
Turney, P.D., Pantel, P.: From frequency to meaning: Vector space models of semantics. J. Artif. Intell. Res. 37, 141–188 (2010). https://www.jair.org/index.php/jair/article/view/10640
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Roux, J., Guenoune, H., Lafourcade, M., Moot, R. (2024). Explaining Metaphors in the French Language by Solving Analogies Using a Knowledge Graph. In: Nöth, E., Horák, A., Sojka, P. (eds) Text, Speech, and Dialogue. TSD 2024. Lecture Notes in Computer Science(), vol 15048. Springer, Cham. https://doi.org/10.1007/978-3-031-70563-2_4
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