Polysemy and synonymy are two crucial interrelated facets of lexical
ambiguity. While both phenomena have been studied extensively in NLP, leading
to dedicated systems, they are often been considered independently. While many
tasks dealing with polysemy (e.g. Word Sense Disambiguiation or Induction)
highlight the role of a word's senses, the study of synonymy is rooted in the
study of concepts, i.e. meaning shared across the lexicon. In this paper, we
introduce Concept Induction, the unsupervised task of learning a soft
clustering among words that defines a set of concepts directly from data. This
task generalizes that of Word Sense Induction. We propose a bi-level approach
to Concept Induction that leverages both a local lemma-centric view and a
global cross-lexicon perspective to induce concepts. We evaluate the obtained
clustering on SemCor's annotated data and obtain good performances (BCubed F1
above 0.60). We find that the local and the global levels are mutually
beneficial to induce concepts and also senses in our setting. Finally, we
create static embeddings representing our induced concepts and use them on the
Word-in-Context task, obtaining competitive performances with the
State-of-the-Art.

通过本文，我们引入了概念归纳这个非监督任务，从数据中学习一组定义概念的词语软聚类，该任务概括了词义归纳的任务。我们提出了一种双层方法来进行概念归纳，充分利用了本地词元为中心和全局跨词库视角来引导概念的生成。我们在 SemCor 的注释数据上评估所得到的聚类，并获得了良好的性能（BCubed F1 大于 0.60）。我们发现在我们的设置中，本地和全局层次相互有益于概念和词义的生成。最后，我们创建了表示我们引入的概念的静态嵌入，并在上下文任务中使用它们，取得了与现有技术相媲美的性能。

通过语境化语言模型诱导概念：从词义到更多

To Word Senses and Beyond: Inducing Concepts with Contextualized  Language Models

Lexical Semantic Change is the study of how the meaning of words evolves
through time. Another related question is whether and how lexical relations
over pairs of words, such as synonymy, change over time. There are currently
two competing, apparently opposite hypotheses in the historical linguistic
literature regarding how synonymous words evolve: the Law of Differentiation
(LD) argues that synonyms tend to take on different meanings over time, whereas
the Law of Parallel Change (LPC) claims that synonyms tend to undergo the same
semantic change and therefore remain synonyms. So far, there has been little
research using distributional models to assess to what extent these laws apply
on historical corpora. In this work, we take a first step toward detecting
whether LD or LPC operates for given word pairs. After recasting the problem
into a more tractable task, we combine two linguistic resources to propose the
first complete evaluation framework on this problem and provide empirical
evidence in favor of a dominance of LD. We then propose various computational
approaches to the problem using Distributional Semantic Models and grounded in
recent literature on Lexical Semantic Change detection. Our best approaches
achieve a balanced accuracy above 0.6 on our dataset. We discuss challenges
still faced by these approaches, such as polysemy or the potential confusion
between synonymy and hypernymy.

本文提出了一个关于词对中同义词语义演变的权衡法则的评价框架，利用分布语义模型提供了证据并探讨了挑战，结果支持了不同化法则的主导作用。

语义变化的两个定律：用分布式语义模型预测同义词的变化

A Tale of Two Laws of Semantic Change: Predicting Synonym Changes with  Distributional Semantic Models

Synonymy and translational equivalence are the relations of sameness of
meaning within and across languages. As the principal relations in wordnets and
multi-wordnets, they are vital to computational lexical semantics, yet the
field suffers from the absence of a common formal framework to define their
properties and relationship. This paper proposes a unifying treatment of these
two relations, which is validated by experiments on existing resources. In our
view, synonymy and translational equivalence are simply different types of
semantic identity. The theory establishes a solid foundation for critically
re-evaluating prior work in cross-lingual semantics, and facilitating the
creation, verification, and amelioration of lexical resources.

本文提出了一种统一处理同义词和翻译等效性这两种语言关系的方法，即将它们视为不同类型的语义同一性，并通过对现有资源的实验进行验证，为跨语言语义学中的先前工作重新评估和词汇资源的创建、验证和改善建立了一个坚实的基础。