Exploring Surface-Level Heuristics for Negation and Speculation Discovery in Clinical Texts

We investigate the automatic identification of negated and speculative statements in biomedical texts, focusing on the clinical domain. Our goal is to evaluate the performance of simple, Regex-based algorithms that have the advantage of low computational cost, simple implementation, and do not rely on the accurate computation of deep linguistic features of idiosyncratic clinical texts. The performance of the NegEx algorithm with an additional set of Regex-based rules reveals promising results (evaluated on the BioScope corpus). Current and future work focuses on a bootstrapping algorithm for the discovery of new rules from unannotated clinical texts.