Many studies were recently done for investigating the properties of
contextual language models but surprisingly, only a few of them consider the
properties of these models in terms of semantic similarity. In this article, we
first focus on these properties for English by exploiting the distributional
principle of substitution as a probing mechanism in the controlled context of
SemCor and WordNet paradigmatic relations. Then, we propose to adapt the same
method to a more open setting for characterizing the differences between static
and contextual language models.

本文研究了上下文语言模型的语义相似性属性，并利用 SemCor 和 WordNet 方式进行探索。然后，将该方法应用于更开放的环境，以表征静态和上下文语言模型之间的差异。

使用分布式原理对上下文语言模型进行语义研究

Using Distributional Principles for the Semantic Study of Contextual  Language Models

This paper presents a method for the resolution of lexical ambiguity of nouns
and its automatic evaluation over the Brown Corpus. The method relies on the
use of the wide-coverage noun taxonomy of WordNet and the notion of conceptual
distance among concepts, captured by a Conceptual Density formula developed for
this purpose. This fully automatic method requires no hand coding of lexical
entries, hand tagging of text nor any kind of training process. The results of
the experiments have been automatically evaluated against SemCor, the
sense-tagged version of the Brown Corpus.

本文提出一种方法解决名词的词汇歧义问题，并对其在布朗语料库上的自动评估进行了说明，方法基于 WordNet 的广泛覆盖的名词分类法和概念密度公式，无需手动编码或训练，结果已自动评估了布朗语料库的语义标记版本 SemCor。