A unified framework for scope learning via simplified shallow semantic parsing

This paper approaches the scope learning problem via simplified shallow semantic parsing. This is done by regarding the cue as the predicate and mapping its scope into several constituents as the arguments of the cue. Evaluation on the BioScope corpus shows that the structural information plays a critical role in capturing the relationship between a cue and its dominated arguments. It also shows that our parsing approach significantly outperforms the state-of-the-art chunking ones. Although our parsing approach is only evaluated on negation and speculation scope learning here, it is portable to other kinds of scope learning.