In this paper, we propose methods for discovering semantic differences in words appearing in two corpora based on the norms of contextualized word vectors. The key idea is that the coverage of meanings is reflected in the norm of its mean word vector. The proposed methods do not require the assumptions concerning words and corpora for comparison that the previous methods do. All they require are to compute the mean vector of contextualized word vectors and its norm for each word type. Nevertheless, they are (i) robust for the skew in corpus size; (ii) capable of detecting semantic differences in infrequent words; and (iii) effective in pinpointing word instances that have a meaning missing in one of the two corpora for comparison. We show these advantages for native and non-native English corpora and also for historical corpora.

翻译：本文提出基于上下文词向量范数发现两个语料库中词语语义差异的方法。核心思想在于：词语义覆盖范围反映在其均值词向量的范数中。所提方法无需前人方法中关于词语及比较语料库的假设条件，仅需计算每个词类型的上下文词向量均值及其范数。尽管如此，该方法具有以下优势：(i) 对语料库规模偏差具有鲁棒性；(ii) 能够检测低频词语的语义差异；(iii) 可有效定位在比较语料库之一中缺失特定语义的词语实例。我们通过英语母语者与非母语者语料库以及历时语料库验证了这些优势。