We propose using automatically generated natural language definitions of
contextualised word usages as interpretable word and word sense
representations. Given a collection of usage examples for a target word, and
the corresponding data-driven usage clusters (i.e., word senses), a definition
is generated for each usage with a specialised Flan-T5 language model, and the
most prototypical definition in a usage cluster is chosen as the sense label.
We demonstrate how the resulting sense labels can make existing approaches to
semantic change analysis more interpretable, and how they can allow users --
historical linguists, lexicographers, or social scientists -- to explore and
intuitively explain diachronic trajectories of word meaning. Semantic change
analysis is only one of many possible applications of the `definitions as
representations' paradigm. Beyond being human-readable, contextualised
definitions also outperform token or usage sentence embeddings in
word-in-context semantic similarity judgements, making them a new promising
type of lexical representation for NLP.

利用自动生成的自然语言定义，将上下文单词用法作为可解释的词和词义表示。使用 Flan-T5 语言模型生成每个用法的定义，从用法簇中选择最典型的定义作为词义标签，可在语义变化分析中进行应用。此外，上下文定义在 NLP 中的单词语境语义相似性判断中优于令牌或用法句子嵌入，是一种新的有前途的词汇表示类型。