the training process for the Joshua machine translation system [@Li:2009] used in the SCALE workshop included MN annotation.

We were particularly interested in identifying modalities and negation because they can be used to characterize events in a variety of automated analytic processes. Modalities and negation can distinguish realized events from unrealized events, beliefs from certainties, and can distinguish positive and negative instances of entities and events. For example, the correct identification and retention of negation in a particular language—such as a single instance of the word “not”—is very important for a correct representation of events and likewise for translation.

The next two sections examine related work and the motivation behind the SIMT approach. Section \[modality-section\] defines the theoretical framework for our MN lexicon and automatic MN taggers. Section \[modality-annotation-scheme\] presents the MN annotation scheme used by our human annotators and describes the creation of a MN lexicon based on this scheme. Section \[Automatic-Modality-Annotation\] presents two types of MN taggers—one that is string-based and one that is structure-based—and evaluates the effectiveness of the structure-based tagger. Section \[joshua\] then presents implementation details of the semantically-informed syntactic system and describes the results of its application. Finally, Section \[conclusions\] presents conclusions and future work.

