论文标题
置换矩阵统计和计算语言任务
Permutation invariant matrix statistics and computational language tasks
论文作者
论文摘要
Kartsaklis，Ramgoolam和Sadrzadeh介绍的语言矩阵理论计划是基于类型驱动的分布语义中生成的矩阵统计数据的方法，基于置换不变的多项式函数，这些函数被视为编码重要统计数据的关键观察值。在本文中，我们概括了先前的结果，即由组成分布语义引起的基质分布的近似高斯性。我们还介绍了单词可观察的向量的几何形状，该几何通过利用图形理论基础的置换式不变性和与单词相关的矩阵集合的统计特征来定义。我们描述了该统一框架在计算语言学中的许多任务中的成功应用，这与同义词，反义词，超ny词和hyphy词之间的区别有关。
The Linguistic Matrix Theory programme introduced by Kartsaklis, Ramgoolam and Sadrzadeh is an approach to the statistics of matrices that are generated in type-driven distributional semantics, based on permutation invariant polynomial functions which are regarded as the key observables encoding the significant statistics. In this paper we generalize the previous results on the approximate Gaussianity of matrix distributions arising from compositional distributional semantics. We also introduce a geometry of observable vectors for words, defined by exploiting the graph-theoretic basis for the permutation invariants and the statistical characteristics of the ensemble of matrices associated with the words. We describe successful applications of this unified framework to a number of tasks in computational linguistics, associated with the distinctions between synonyms, antonyms, hypernyms and hyponyms.
