A Modality Lexicon and its use in Automatic Tagging
Kathryn Baker, Michael Bloodgood, Bonnie Jean Dorr, Nathaniel Wesley Filardo, Lori S. Levin, Christine Piatko · 2010
This paper describes our resource-building results for an eight-week JHU Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation.Specifically, we describe the construction of a modality annotation scheme, a modality lexicon, and two automated modality taggers that were built using the lexicon and annotation scheme.Our annotation scheme is based on identifying three components of modality: a trigger, a target and a holder.We describe how our modality lexicon was produced semi-automatically, expanding from an initial hand-selected list of modality trigger words and phrases.The resulting expanded modality lexicon is being made publicly available.We demonstrate that one tagger-a structure-based tagger-results in precision around 86% (depending on genre) for tagging of a standard LDC data set.In a machine translation application, using the structure-based tagger to annotate English modalities on an English-Urdu training corpus improved the translation quality score for Urdu by 0.3 Bleu points in the face of sparse training data.