and not applicable [@Diab:2009], and derive features for machine learning from parse trees. The authors desire to combine their work with FactBank annotation.

The CoNLL-2010 shared task [@Farkas:2010] was about the detection of cues for uncertainty and their scope. The task was described as “hedge detection,” that is, finding statements which do not or cannot be backed up with facts. Auxiliary verbs such as [*may*]{}, [*might*]{}, [*can*]{}, etc. are one type of hedge cue. The training data for the shared task included the BioScope corpus [@Szarvas:2008], which is manually annotated with negation and speculation cues and their scope, and paragraphs from Wikipedia possibly containing hedge information. Our scheme also identifies cues in the form of triggers, but our desired outcome is to cover the full range of modalities and not just certainty and uncertainty. To identify scope, we use syntactic parse trees, as was allowed in the CoNLL task.

The textual entailment literature includes modality annotation schemes. Identifying modalities is important to determine whether a text entails a hypothesis. Bar-Haim et al. include polarity based rules and negation and modality annotation rules. The polarity rules are based on an independent polarity lexicon [@Nairn:2006]. The annotation rules for negation and modality 