Abstract
In this paper we introduce a system for the computation of explanations that accompany scores in the conceptual similarity task. In this setting the problem is, given a pair of concepts, to provide a score that expresses in how far the two concepts are similar. In order to explain how explanations are automatically built, we illustrate some basic features of COVER, the lexical resource that underlies our approach, and the main traits of the MeRaLi system, that computes conceptual similarity and explanations, all in one. To assess the computed explanations, we have designed a human experimentation, that provided interesting and encouraging results, which we report and discuss in depth.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
COVER is available for download at http://ls.di.unito.it.
- 2.
InstanceOf, RelatedTo, IsA, AtLocation, dbpedia/genre, Synonym, DerivedFrom, Causes, UsedFor, MotivatedByGoal, HasSubevent, Antonym, CapableOf, Desires, CausesDesire, PartOf, HasProperty, HasPrerequisite, MadeOf, CompoundDerivedFrom, HasFirstSubevent, dbpedia/field, dbpedia/knownFor, dbpedia/influencedBy, dbpedia/influenced, DefinedAs, HasA, MemberOf, ReceivesAction, SimilarTo, dbpedia/influenced, SymbolOf, HasContext, NotDesires, ObstructedBy, HasLastSubevent, NotUsedFor, NotCapableOf, DesireOf, NotHasProperty, CreatedBy, Attribute, Entails, LocationOfAction, LocatedNear.
- 3.
The parameters \(\alpha \) and \(\beta \) were set to .8 and .2 for the experimentation, based on a parameter tuning performed on the RG, MC and WS-Sim datasets [17].
- 4.
Actually the pair \(\langle \)mojito,mohito\(\rangle \) was dropped in that ‘mojito’ was not recognised as a morphological variant of ‘mohito’ by most participants.
- 5.
References
Camacho-Collados, J., Pilehvar, M.T., Collier, N., Navigli, R.: SemEval-2017 task 2: multilingual and cross-lingual semantic word similarity. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval 2017), Vancouver, Canada, pp. 15–26 (2017)
Camacho-Collados, J., Pilehvar, M.T., Navigli, R.: NASARI: a novel approach to a semantically-aware representation of items. In: Proceedings of NAACL, pp. 567–577 (2015)
Cambria, E., Speer, R., Havasi, C., Hussain, A.: SenticNet: a publicly available semantic resource for opinion mining. AAAI fall Symp. Commonsense Knowl. 10, 14–18 (2010)
Colla, D., Mensa, E., Radicioni, D.P.: Semantic measures for keywords extraction. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds.) AI*IA 2017. LNCS, vol. 10640, pp. 128–140. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70169-1_10
Gärdenfors, P.: The Geometry of Meaning: Semantics Based on Conceptual Spaces. MIT Press, Cambridge (2014)
Gatt, A., Reiter, E.: SimpleNLG: a realisation engine for practical applications. In: Proceedings of the 12th European Workshop on Natural Language Generation, pp. 90–93. Association for Computational Linguistics (2009)
Ghignone, L., Lieto, A., Radicioni, D.P.: Typicality-based inference by plugging conceptual spaces into ontologies. In: AIC@ AI* IA, vol. 1100, pp. 68–79 (2013)
Harabagiu, S., Moldovan, D.: Question answering. In: The Oxford Handbook of Computational Linguistics (2003)
Havasi, C., Speer, R., Alonso, J.: ConceptNet: a lexical resource for common sense knowledge. Recent Adv. Nat. Lang. Process. V: Sel. Pap. RANLP 309, 269–280 (2007)
Hovy, E.: Text summarization. In: The Oxford Handbook of Computational Linguistics 2nd edition (2003)
Jimenez, S., Becerra, C., Gelbukh, A., Bátiz, A.J.D., Mendizábal, A.: Softcardinality-core: improving text overlap with distributional measures for semantic textual similarity. In: Proceedings of *SEM 2013, vol. 1, pp. 194–201 (2013)
Lieto, A., Mensa, E., Radicioni, D.P.: A resource-driven approach for anchoring linguistic resources to conceptual spaces. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 435–449. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49130-1_32
Lieto, A., Minieri, A., Piana, A., Radicioni, D.P.: A knowledge-based system for prototypical reasoning. Connection Sci. 27(2), 137–152 (2015)
Lieto, A., Radicioni, D.P., Rho, V.: Dual PECCS: a cognitive system for conceptual representation and categorization. J. Exp. Theor. Artif. Intell. 29(2), 433–452 (2017)
Lombardo, V., Piana, F., Mimmo, D., Mensa, E., Radicioni, D.P.: Semantic models for the geological mapping process. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds.) AI*IA 2017, vol. 10640, pp. 295–306. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70169-1_22
Marujo, L., Ribeiro, R., de Matos, D.M., Neto, J.P., Gershman, A., Carbonell, J.: Key phrase extraction of lightly filtered broadcast news. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2012. LNCS (LNAI), vol. 7499, pp. 290–297. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32790-2_35
Mensa, E., Radicioni, D.P., Lieto, A.: MERALI at SemEval-2017 task 2 subtask 1: a cognitively inspired approach. In: Proceedings of SemEval-2017, pp. 236–240. ACL (2017)
Mensa, E., Radicioni, D.P., Lieto, A.: TTCS\(^{\cal{E}}\): a vectorial resource for computing conceptual similarity. In: EACL 2017 Workshop on Sense, Concept and Entity Representations and their Applications, pp. 96–101. ACL (2017)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Minsky, M.: A framework for representing knowledge. In: Winston, P. (ed.) The Psychology of Computer Vision, pp. 211–277. McGraw-Hill, New York (1975)
Moulin, B., Irandoust, H., Bélanger, M., Desbordes, G.: Explanation and argumentation capabilities: towards the creation of more persuasive agents. Artif. Intell. Rev. 17(3), 169–222 (2002)
Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)
Resnick, L.B., Salmon, M., Zeitz, C.M., Wathen, S.H., Holowchak, M.: Reasoning in conversation. Cogn. Instr. 11(3–4), 347–364 (1993)
Rosch, E.: Cognitive representations of semantic categories. J. Exp. Psychol. Gen. 104(3), 192–233 (1975)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surveys (CSUR) 34(1), 1–47 (2002)
Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327–352 (1977)
Acknowledgements
We desire to thank Simone Donetti and the Technical Staff of the Computer Science Department of the University of Turin, for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Colla, D., Mensa, E., Radicioni, D.P., Lieto, A. (2018). Tell Me Why: Computational Explanation of Conceptual Similarity Judgments. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-91473-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91472-5
Online ISBN: 978-3-319-91473-2
eBook Packages: Computer ScienceComputer Science (R0)