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.
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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.
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Acknowledgements
We desire to thank Simone Donetti and the Technical Staff of the Computer Science Department of the University of Turin, for their support.
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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
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