Lexicons are indispensable tools for textual analysis through labelled term associations. Despite their utility, lexicons are static and require manual effort to curate and maintain. Regular updates are essential to stay relevant amid semantic shifts and neologisms during language evolution. In this work, we explore the potential of supervised learning for expanding lexicons using dictionaries. We study the effect of using dictionaries with varying properties such as noise, size, labels, structure and curation method. Definitions are used as input features to a transformer model (BERT) that assigns categories to terms. We analyse the expansions using varying English dictionaries and lexicons for estimated accuracy, coverage and labelling consistency and apply the expanded versions to a downstream task. Our analyses show dictionary based expansion is a robust approach. We release our expanded lexicons, code, and pretrained models.

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