159,879 research outputs found
Introducing fuzzy trust for managing belief conflict over semantic web data
Interpreting Semantic Web Data by different human experts can end up in scenarios, where each expert comes up with different and conflicting ideas what a concept can mean and how they relate to other concepts. Software agents that operate on the Semantic Web have to deal with similar scenarios where the interpretation of Semantic Web data that describes the heterogeneous sources becomes contradicting. One such application area of the Semantic Web is ontology mapping where different similarities have to be combined into a more reliable and coherent view, which might easily become unreliable if the conflicting
beliefs in similarities are not managed effectively between the different agents. In this paper we propose a solution for managing this conflict by introducing trust between the mapping agents based on the fuzzy voting model
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing
facilitate conceptual grounding of language. We categorize the information flow
in multimodal processing with respect to cognitive models of human information
processing and analyze different methods for combining multimodal
representations. Based on this methodological inventory, we discuss the benefit
of multimodal grounding for a variety of language processing tasks and the
challenges that arise. We particularly focus on multimodal grounding of verbs
which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference
of Computational Linguistics. Please refer to this version for citations:
https://www.aclweb.org/anthology/papers/C/C18/C18-1197
Modal Similarity
Just as Boolean rules define Boolean categories, the Boolean operators define higher-order Boolean categories referred to as modal categories. We examine the similarity order between these categories and the standard category of logical identity (i.e. the modal category defined by the biconditional or equivalence operator). Our goal is 4-fold: first, to introduce a similarity measure for determining this similarity order; second, to show that such a measure is a good predictor of the similarity assessment behaviour observed in our experiment involving key modal categories; third, to argue that as far as the modal categories are concerned, configural similarity assessment may be componential or analytical in nature; and lastly, to draw attention to the intimate interplay that may exist between deductive judgments, similarity assessment and categorisation
Polarization of coalitions in an agent-based model of political discourse
Political discourse is the verbal interaction between political actors in a policy domain. This article explains the formation of polarized advocacy or discourse coalitions in this complex phenomenon by presenting a dynamic, stochastic, and discrete agent-based model based on graph theory and local optimization. In a series of thought experiments, actors compute their utility of contributing a specific statement to the discourse by following ideological criteria, preferential attachment, agenda-setting strategies, governmental coherence, or other mechanisms. The evolving macro-level discourse is represented as a dynamic network and evaluated against arguments from the literature on the policy process. A simple combination of four theoretical mechanisms is already able to produce artificial policy debates with theoretically plausible properties. Any sufficiently realistic configuration must entail innovative and path-dependent elements as well as a blend of exogenous preferences and endogenous opinion formation mechanisms
Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning
Distributional word representation methods exploit word co-occurrences to
build compact vector encodings of words. While these representations enjoy
widespread use in modern natural language processing, it is unclear whether
they accurately encode all necessary facets of conceptual meaning. In this
paper, we evaluate how well these representations can predict perceptual and
conceptual features of concrete concepts, drawing on two semantic norm datasets
sourced from human participants. We find that several standard word
representations fail to encode many salient perceptual features of concepts,
and show that these deficits correlate with word-word similarity prediction
errors. Our analyses provide motivation for grounded and embodied language
learning approaches, which may help to remedy these deficits.Comment: Accepted at RoboNLP 201
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