Paper: Learning the Scope of Negation in Biomedical Texts

ACL ID D08-1075
Title Learning the Scope of Negation in Biomedical Texts
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008

In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system consists of two memory-based engines, one that decides if the tokens in a sentence are negation signals, and another that finds the full scope of these nega- tion signals. Our approach to negation detec- tion differs in two main aspects from existing research on negation. First, we focus on find- ing the scope of negation signals, instead of determining whether a term is negated or not. Second, we apply supervised machine learn- ing techniques, whereas most existing systems apply rule-based algorithms. As far as we know, this way of approaching the negation scope finding task is novel.