Semantically Enriched Models for Modal Sense Classification

Modal verbs have different interpretations depending on their context. Previous approaches to modal sense classification achieve relatively high performance using shallow lexical and syntactic features. In this work we uncover the difficulty of particular modal sense distinctions by eliminating both distributional bias and sparsity of existing small-scale annotated corpora used in prior work. We build a semantically enriched model for modal sense classification by novelly applying features that relate to lexical, proposition-level, and discourse-level semantic factors. Besides improved classification performance, especially for difficult sense distinctions, closer examination of interpretable feature sets allows us to obtain a better understanding of relevant semantic and contextual factors in modal sense classification.