Word meaning is notoriously difficult to capture, both synchronically and
diachronically. In this paper, we describe the creation of the largest resource
of graded contextualized, diachronic word meaning annotation in four different
languages, based on 100,000 human semantic proximity judgments. We thoroughly
describe the multi-round incremental annotation process, the choice for a
clustering algorithm to group usages into senses, and possible - diachronic and
synchronic - uses for this dataset.

本文描述了基于 10 万个人类语义接近度判断的四种语言的等级背景下的历时词汇意义注释的最大资源的创建过程，包括多轮增量注释过程，聚类算法选择将用法分组为意义的可能性以及数据集的历时和同步用法。

DWUG: 四种语言的大规模历时词汇使用图资源

DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages

We propose a new method that leverages contextual embeddings for the task of
diachronic semantic shift detection by generating time specific word
representations from BERT embeddings. The results of our experiments in the
domain specific LiverpoolFC corpus suggest that the proposed method has
performance comparable to the current state-of-the-art without requiring any
time consuming domain adaptation on large corpora. The results on the newly
created Brexit news corpus suggest that the method can be successfully used for
the detection of a short-term yearly semantic shift. And lastly, the model also
shows promising results in a multilingual settings, where the task was to
detect differences and similarities between diachronic semantic shifts in
different languages.

本文提出了一种新的方法，通过从 BERT 嵌入生成特定时间的单词表示来利用上下文嵌入进行历时语义变化检测。我们的实验结果表明，该方法在特定领域的 LiverpoolFC 语料库中具有与当前最先进技术相当的性能，无需在大型语料库上进行任何耗时的领域自适应。我们的结果表明该方法可以成功地用于检测短期年度语义漂移，并在多语言环境中展示了有前途的结果。

利用语境嵌入检测历时语义转移

Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift

Recent years have witnessed a surge of publications aimed at tracing temporal
changes in lexical semantics using distributional methods, particularly
prediction-based word embedding models. However, this vein of research lacks
the cohesion, common terminology and shared practices of more established areas
of natural language processing. In this paper, we survey the current state of
academic research related to diachronic word embeddings and semantic shifts
detection. We start with discussing the notion of semantic shifts, and then
continue with an overview of the existing methods for tracing such time-related
shifts with word embedding models. We propose several axes along which these
methods can be compared, and outline the main challenges before this emerging
subfield of NLP, as well as prospects and possible applications.

本文综述了当前关于使用预测型词嵌入模型追踪词汇语义时序变化和语义漂移检测的学术研究现状，并讨论了该领域的挑战和应用前景。