Abstract
Semantic distance measurement is a fundamental problem in computational linguistics, providing a quantitative characterization of similarity or relatedness between text segments, and underpinning tasks such as text retrieval and text classification. From a mathematical perspective, a semantic distance can be viewed as a metric defined on a space of texts or on a representation space derived from them. However, most classical semantic distance methods are essentially fixed, making them difficult to adapt to specific data distributions and task requirements. In this paper, a semantic distance measure based on multi-kernel Gaussian processes (MK-GP) was proposed. The latent semantic function associated with texts was modeled as a Gaussian process, with its covariance function given by a combined kernel combining Matérn and polynomial components. The kernel parameters were learned automatically from data under supervision, rather than being hand-crafted. This semantic distance was instantiated and evaluated in the context of fine-grained sentiment classification with large language models under an in-context learning (ICL) setup. The experimental results demonstrated the effectiveness of the proposed measure.
Abstract (translated)
语义距离测量是计算语言学中的一个基本问题，它提供了一种对文本片段之间相似性或相关性的定量描述，并支持诸如文本检索和分类等任务。从数学角度来看，语义距离可以被视为在文本空间或由其衍生的表示空间上定义的一种度量标准。然而，大多数传统的语义距离方法本质上是固定的，这使得它们难以适应特定的数据分布和任务需求。 本文提出了一种基于多核高斯过程（MK-GP）的语义距离测量方法。该方法将与文本相关的潜在语义函数建模为高斯过程，并使用由Matérn核和多项式成分结合而成的协方差函数来定义其内核参数。这些内核参数是从数据中自动学习得到的，而不是手工设置的。这种语义距离在大规模语言模型的细粒度情感分类任务中的情境学习（ICL）环境中进行了实例化并进行评估。 实验结果表明了所提出的方法的有效性。
URL
https://arxiv.org/abs/2512.12238
