Categorical compositional distributional semantics is an approach to
modelling language that combines the success of vector-based models of meaning
with the compositional power of formal semantics. However, this approach was
developed without an eye to cognitive plausibility. Vector representations of
concepts and concept binding are also of interest in cognitive science, and
have been proposed as a way of representing concepts within a biologically
plausible spiking neural network. This work proposes a way for compositional
distributional semantics to be implemented within a spiking neural network
architecture, with the potential to address problems in concept binding, and
give a small implementation. We also describe a means of training word
representations using labelled images.

提出了一种在脉冲神经网络结构中实现复合分布语义、解决概念绑定问题的方法，利用标记图像进行词向量训练。

基于实践学习的组合向量语义

Grounded learning for compositional vector semantics

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, 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.

本文使用分类组成分布语义模型（categorical compositional distributional semantics）结合 Barwise 和 Cooper 的广义量词理论，进行了基于生成语法形式化的抽象范畴组成语义学习。并证明了基于集合的关系实例化的等价性，以及基于向量空间的实例化的有效性。

具范畴组合分布语义双代数的自然语言广义量词理论

A Generalised Quantifier Theory of Natural Language in Categorical  Compositional Distributional Semantics with Bialgebras

Distributional semantic models provide vector representations for words by
gathering co-occurrence frequencies from corpora of text. Compositional
distributional models extend these from words to phrases and sentences. In
categorical compositional distributional semantics, phrase and sentence
representations are functions of their grammatical structure and
representations of the words therein. In this setting, grammatical structures
are formalised by morphisms of a compact closed category and meanings of words
are formalised by objects of the same category. These can be instantiated in
the form of vectors or density matrices. This paper concerns the applications
of this model to phrase and sentence level entailment. We argue that
entropy-based distances of vectors and density matrices provide a good
candidate to measure word-level entailment, show the advantage of density
matrices over vectors for word level entailments, and prove that these
distances extend compositionally from words to phrases and sentences. We
exemplify our theoretical constructions on real data and a toy entailment
dataset and provide preliminary experimental evidence.

本文介绍了基于矢量表示的分布语义模型，扩展到短语和句子，提出了分类组合分布语义学理论框架，实现了短语和句子层面的言外推理，并通过实验论证了基于密度矩阵的熵距离在词语和短语、句子层面的言外推理可行性和优越性。