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Formalising Concepts as Grounded Abstractions ...
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Abstract:
The notion of concept has been studied for centuries, by philosophers, linguists, cognitive scientists, and researchers in artificial intelligence (Margolis & Laurence, 1999). There is a large literature on formal, mathematical models of concepts, including a whole sub-field of AI -- Formal Concept Analysis -- devoted to this topic (Ganter & Obiedkov, 2016). Recently, researchers in machine learning have begun to investigate how methods from representation learning can be used to induce concepts from raw perceptual data (Higgins, Sonnerat, et al., 2018). The goal of this report is to provide a formal account of concepts which is compatible with this latest work in deep learning. The main technical goal of this report is to show how techniques from representation learning can be married with a lattice-theoretic formulation of conceptual spaces. The mathematics of partial orders and lattices is a standard tool for modelling conceptual spaces (Ch.2, Mitchell (1997), Ganter and Obiedkov (2016)); however, ...
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Keyword:
Artificial Intelligence cs.AI; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2101.05125 https://arxiv.org/abs/2101.05125
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Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input ...
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Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing ...
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Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns ...
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Modelling metaphor with attribute-based semantics
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Bulat, L; Clark, Stephen; Shutova, Ekaterina. - : Association for Computational Linguistics, 2017. : Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2017
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Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research ...
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Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics ...
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Multi-Modal Representations for Improved Bilingual Lexicon Learning ...
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Multi-Modal Representations for Improved Bilingual Lexicon Learning
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Vulić, I; Kiela, D; Clark, Stephen. - : Association for Computational Linguistics, 2016. : http://acl2016.org/, 2016. : Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016
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Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics
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Kiela, D; Vero, Anita; Clark, Stephen. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016
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RELPRON: A Relative Clause Evaluation Dataset for Compositional Distributional Semantics
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Vision and Feature Norms: Improving automatic feature norm learning through cross-modal maps
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Bulat, L; Kiela, D; Clark, Stephen. - : Association for Computational Linguistics, 2016. : http://www.aclweb.org/anthology/N/N16/, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
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Expected F-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks
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Xu, W; Auli, M; Clark, Stephen. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
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Using Sentence Plausibility to Learn the Semantics of Transitive Verbs ...
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The Frobenius anatomy of word meanings I: subject and object relative pronouns ...
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Participant Reference in Narrative Discourse: A Comparison of Three Methodologies
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Concrete Sentence Spaces for Compositional Distributional Models of Meaning ...
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