A widely acknowledged shortcoming of WordNet is that it lacks a distinction
between word meanings which are systematically related (polysemy), and those
which are coincidental (homonymy). Several previous works have attempted to
fill this gap, by inferring this information using computational methods. We
revisit this task, and exploit recent advances in language modelling to
synthesise homonymy annotation for Princeton WordNet. Previous approaches treat
the problem using clustering methods; by contrast, our method works by linking
WordNet to the Oxford English Dictionary, which contains the information we
need. To perform this alignment, we pair definitions based on their proximity
in an embedding space produced by a Transformer model. Despite the simplicity
of this approach, our best model attains an F1 of .97 on an evaluation set that
we annotate. The outcome of our work is a high-quality homonymy annotation
layer for Princeton WordNet, which we release.

本文重新访问了填补 WordNet 中同音异义词及多义词间差异的问题，并且利用语言模型对此进行了合成标注。

英语 WordNet 的同音词信息

Homonymy Information for English WordNet

One of the central aspects of contextualised language models is that they
should be able to distinguish the meaning of lexically ambiguous words by their
contexts. In this paper we investigate the extent to which the contextualised
embeddings of word forms that display multiplicity of sense reflect traditional
distinctions of polysemy and homonymy. To this end, we introduce an extended,
human-annotated dataset of graded word sense similarity and co-predication
acceptability, and evaluate how well the similarity of embeddings predicts
similarity in meaning. Both types of human judgements indicate that the
similarity of polysemic interpretations falls in a continuum between identity
of meaning and homonymy. However, we also observe significant differences
within the similarity ratings of polysemes, forming consistent patterns for
different types of polysemic sense alternation. Our dataset thus appears to
capture a substantial part of the complexity of lexical ambiguity, and can
provide a realistic test bed for contextualised embeddings. Among the tested
models, BERT Large shows the strongest correlation with the collected word
sense similarity ratings, but struggles to consistently replicate the observed
similarity patterns. When clustering ambiguous word forms based on their
embeddings, the model displays high confidence in discerning homonyms and some
types of polysemic alternations, but consistently fails for others.

本文研究了语境化语言模型的多义词辨别能力。作者提出了一个人工注释的数据集，用于评估多义词词义的相似性，发现多义词的词义相似性介于相同和同音异义之间，并呈现出一定的词义变化规律性。BERT Large 在相似性等级方面表现最佳，但无法一致地复制观察到的相似性模式，并且在某些类型的多义词变化上存在问题。