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Semantic Classification and Learning Using a Linear Tranformation Model in a Probabilistic Type Theory with Records
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- Larsson, Staffan, 1969 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för filosofi, lingvistik och vetenskapsteori,Department of Philosophy, Linguistics and Theory of Science
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- Bernardy, Jean-Philippe, 1978 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för filosofi, lingvistik och vetenskapsteori,Department of Philosophy, Linguistics and Theory of Science
- (creator_code:org_t)
- Stroudsburg, PA, USA : Association for Computational Linguistics, 2021
- 2021
- English.
- In: ReInAct 2021. Proceedings of the Conference on Reasoning and Interaction, Gothenburg and online 4–6 October, 2021 / Christine Howes, Simon Dobnik, Ellen Breitholtz and Stergios Chatzikyriakidis (eds.). - Stroudsburg, PA, USA : Association for Computational Linguistics. - 9781955917070
- Conference paper (peer-reviewed)
Abstract
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- Starting from an existing account of semantic classification and learning from interaction formulated in a Probabilistic Type Theory with Records, encompassing Bayesian inference and learning with a frequentist flavour, we observe some problems with this account and provide an alternative account of classification learning that addresses the observed problems. The proposed account is also broadly Bayesian in nature but instead uses a linear transformation model for classification and learning.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
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