Speculation and Negation: Rules, Rankers, and the Role of Syntax

Abstract

This article explores a combination of deep and shallow approaches to the problem of resolving the scope of speculation and negation within a sentence, specifically within the domain of biomedical research literature. The first part of the article focuses on speculation. After first showing how speculation cues can be accurately identified using a very simple classifier informed only by local lexical context, we go on to explore two different syntactic approaches to resolving the in-sentence scopes of these cues. While one uses manually crafted rules operating over dependency structures, the other uses an automatically learned ranking function over nodes in constituent trees. We provide an in-depth error analysis and discussion of various linguistic properties characterizing the problem, and show that although both approaches yield good results in isolation, even better results can be obtained by combining the two, yielding the best published results to date on the CoNLL 2010 Shared Task test data.

In the last part of the article we show how the core of our speculation system, the dependency rules and the cue classifier, can be ported to handle negation as well. With only modest modifications to the initial design, the system also achieves (at least) state-of-the-art results on the negation task.