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A generalised quantifier theory of natural language in categorical compositional distributional semantics with bialgebras

机译:具有双代数的类别组成分布语义中的自然语言广义量词理论

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Categorical compositional distributional semantics is a model of natural language; it combines the statistical vector space models of words with the compositional models of grammar. We formalise in this model the generalised quantifier theory of natural language, due to Barwise and Cooper. The underlying setting is a compact closed category with bialgebras. We start from a generative grammar formalisation and develop an abstract categorical compositional semantics for it, and then instantiate the abstract setting to sets and relations and to finite-dimensional vector spaces and linear maps. We prove the equivalence of the relational instantiation to the truth theoretic semantics of generalised quantifiers. The vector space instantiation formalises the statistical usages of words and enables us to, for the first time, reason about quantified phrases and sentences compositionally in distributional semantics.
机译:分类组成分布语义是自然语言的模型;它结合了词的统计向量空间模型和语法组成模型。由于Barwise和Cooper,我们在此模型中形式化了自然语言的广义量词理论。基本设置是带有双代数的紧致封闭类别。我们从生成语法的形式化开始,并为其开发一种抽象的分类构成语义,然后将抽象设置实例化为集合和关系以及有限维向量空间和线性映射。我们证明了关系实例化与广义量词的真理理论语义的等价性。向量空间实例化正式化了单词的统计用法,并使我们首次能够在分布语义学上对量化短语和句子进行推理。

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