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This paper describes our resource-building results for an eight-week JHUHuman Language Technology Center of Excellence Summer Camp for Applied LanguageExploration (SCALE-2009) on Semantically-Informed Machine Translation.Specifically, we describe the construction of a modality annotation scheme, amodality lexicon, and two automated modality taggers that were built using thelexicon and annotation scheme. Our annotation scheme is based on identifyingthree components of modality: a trigger, a target and a holder. We describe howour modality lexicon was produced semi-automatically, expanding from an initialhand-selected list of modality trigger words and phrases. The resultingexpanded modality lexicon is being made publicly available. We demonstrate thatone tagger---a structure-based tagger---results in precision around 86%(depending on genre) for tagging of a standard LDC data set. In a machinetranslation application, using the structure-based tagger to annotate Englishmodalities on an English-Urdu training corpus improved the translation qualityscore for Urdu by 0.3 Bleu points in the face of sparse training data.

A Modality Lexicon and its use in Automatic Tagging