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Quantization, Frobenius and Bi Algebras from the Categorical Framework of Quantum Mechanics to Natural Language Semantics

机译:从量子力学的分类框架到自然语言语义学的量化,Frobenius和Bi代数

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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.
机译:紧凑封闭类别以及Frobenius和Bi代数已被应用于有关量子协议的模型和推理。相同的构造也已被用于自然语言语义的推理,其名称为:``分类分布组成''语义,或简称为``DisCoCat''模型。该模型将词义的统计矢量模型与语法结构的组成模型相结合。它已应用于自然语言任务,例如消歧,释义和短语和句子的包含。从语法结构到向量的传递是由函子提供的,类似于量子场论的量化函子。原始DisCoCat模型仅使用紧凑型封闭类别。后来,将Frobenius代数添加到其中,以模拟长距离依赖关系,例如相对代词。最近,双代数已被添加到数据包中以说明量词。本文回顾了这些构造及其在自然语言语义学中的应用。我们回顾了这一理论,并提出了一些核心实验结果。

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