基于上下文化词向量的无需训练和词对齐的语义差异发现方法
Contextualized Word Vector-based Methods for Discovering Semantic Differences with No Training nor Word Alignment
AI总结：
本文提出基于上下文化词向量范数的方法，无需训练和词对齐即可发现两个语料库中词语的语义差异，在母语与非母语及历史语料库中验证了其对语料规模偏斜的鲁棒性及低频词检测能力。
摘要：
本文提出基于上下文化词向量范数的方法，用于发现在两个语料库中出现的词语的语义差异。核心思想是，含义的覆盖范围反映在其平均词向量的范数中。所提方法不需要先前方法所要求的关于待比较词语和语料库的假设。它们只需计算每个词类型的上下文化词向量的平均向量及其范数。尽管如此，它们(i)对语料库规模偏斜具有鲁棒性；(ii)能够检测低频词的语义差异；(iii)能有效定位在两个待比较语料库之一中缺失某种含义的词实例。我们在母语和非母语英语语料库以及历史语料库中展示了这些优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
