论文标题
用密度矩阵建模词汇歧义
Modelling Lexical Ambiguity with Density Matrices
论文作者
论文摘要
单词可以具有多种感官。已经有人说，含义的组成分布模型很好地处理了被称为多义的含义变化的细性阴影，但并没有很好地处理词源无关或同义词的单词感官。从向量转移到密度矩阵使我们能够通过单词的不同感官编码概率分布，也可以在含义的组成分布模型中容纳。在本文中，我们介绍了三个新的神经模型，用于从语料库中学习密度矩阵，并测试它们在一系列组成数据集上歧视单词感官的能力。与特定组成方法配对时，我们的最佳模型优于现有的基于向量的构图模型以及强句编码器。
Words can have multiple senses. Compositional distributional models of meaning have been argued to deal well with finer shades of meaning variation known as polysemy, but are not so well equipped to handle word senses that are etymologically unrelated, or homonymy. Moving from vectors to density matrices allows us to encode a probability distribution over different senses of a word, and can also be accommodated within a compositional distributional model of meaning. In this paper we present three new neural models for learning density matrices from a corpus, and test their ability to discriminate between word senses on a range of compositional datasets. When paired with a particular composition method, our best model outperforms existing vector-based compositional models as well as strong sentence encoders.
