Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis
Mario Giulianelli, Iris Luden, Raquel Fernández, Andrey Kutuzov · 2023
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.