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 in 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. Whereas one uses manually crafted rules operating over dependency structures, the other automatically learns a discriminative 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 perform well in isolation, even better results can be obtained by combining them, yielding the best published results to date on the CoNLL-2010 Shared Task data. The last part of the article describes how our speculation system is ported to also resolve the scope of negation. With only modest modifications to the initial design, the system obtains state-of-the-art results on this task also.

Learning Structures of Negations from Flat Annotations

“…As shown in Table 1, our CuePredicate tagger obtained F-measures in the range of state-of-the-art results on negation cue detection using the BioScope (90-96% F-measure (Velldal et al, 2012)). …”

“…It is important to note that the main source of error here is the NegatedPredicate-to-BioScopeScopeSpan trans- Table 3: PCS measures from previous BioScope span detection approaches and our end-to-end system. Col. 1-3: end-to-end systems (Morante and Daelemans, 2009), (Ballesteros et al, 2012), and (Velldal et al, 2012);…”

OPT: Oslo–Potsdam–Teesside. Pipelining Rules, Rankers, and Classifier Ensembles for Shallow Discourse Parsing

“…Explicit Connectives Our classifier for detecting explicit discourse connectives extends the work by Velldal et al (2012) for identifying expressions of speculation and negation. The approach treats the set of connectives observed in the training data as a closed class, and 'only' attempts to disambiguate occurrences of these token sequences in new data.…”

NegMiner: An Automated Tool for Mining Negations from Electronic Narrative Medical Documents

“…This algorithm has been utilized [26] for finding encounter-based events in clinical electronic medical records and for classifying them. The idea of using syntactic and semantic processing for determining the scope of negation cues has been tackled in many other research studies [27][28][29][30][31][32][33][34].…”