Quantization, Frobenius and Bi Algebras from the Categorical Framework of Quantum Mechanics to Natural Language Semantics. Sadrzadeh, M. Frontiers in Physics, 2017. ![Quantization, Frobenius and Bi Algebras from the Categorical Framework of Quantum Mechanics to Natural Language Semantics [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Compact Closed categories and Frobenius and Bi algebras have been applied to model and reason about Quantum protocols. The same constructions have also been applied to reason about natural language semantics under the name: ``categorical distributional compositional'' semantics, or in short, the ``DisCoCat'' model. This model combines the statistical vector models of word meaning with the compositional models of grammatical structure. It has been applied to natural language tasks such as disambiguation, paraphrasing and entailment of phrases and sentences. The passage from the grammatical structure to vectors is provided by a functor, similar to the Quantization functor of Quantum Field Theory. The original DisCoCat model only used compact closed categories. Later, Frobenius algebras were added to it to model long distance dependancies such as relative pronouns. Recently, bialgebras have been added to the pack to reason about quantifiers. This paper reviews these constructions and their application to natural language semantics. We go over the theory and present some of the core experimental results.
@article{Sadrzadeh_quantization:2017,
title = {Quantization, {Frobenius} and {Bi} {Algebras} from the {Categorical} {Framework} of {Quantum} {Mechanics} to {Natural} {Language} {Semantics}},
volume = {5},
issn = {2296-424X},
url = {https://www.frontiersin.org/articles/10.3389/fphy.2017.00018/full},
doi = {10.3389/fphy.2017.00018},
abstract = {Compact Closed categories and Frobenius and Bi algebras have been applied to model and reason about Quantum protocols. The same constructions have also been applied to reason about natural language semantics under the name: ``categorical distributional compositional'' semantics, or in short, the ``DisCoCat'' model. This model combines the statistical vector models of word meaning with the compositional models of grammatical structure. It has been applied to natural language tasks such as disambiguation, paraphrasing and entailment of phrases and sentences. The passage from the grammatical structure to vectors is provided by a functor, similar to the Quantization functor of Quantum Field Theory. The original DisCoCat model only used compact closed categories. Later, Frobenius algebras were added to it to model long distance dependancies such as relative pronouns. Recently, bialgebras have been added to the pack to reason about quantifiers. This paper reviews these constructions and their application to natural language semantics. We go over the theory and present some of the core experimental results.},
language = {English},
urldate = {2021-03-15},
journal = {Frontiers in Physics},
author = {Sadrzadeh, Mehrnoosh},
year = {2017},
keywords = {Bialgebras, Categorical quantum mechanics, Compact closed categories, Frobenius algebras, Natural Language Processing, Pregroup grammars, Quantisation Functor, Vector Space Models, compositionality, distributional semantics},
}
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