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Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT by Kathryn Baker; Michael Bloodgood; Bonnie J. Dorr; Chris Callison-Burch; Nathaniel W. Filardo; Christine Piatko; Lori Levin; Scott Miller is a Computer Science article available to read on EtoBox.

What is Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT about?

This article describes the resource- and system-building efforts of an 8-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically Informed Machine Translation (SIMT). We describe a new modality/negation (MN) annotation scheme, the creation of a (publicly available) MN lexicon, and two automated MN taggers that we built using the annotation scheme and lexicon. Our annotation scheme isolates three components of modality and negation: a trigger (a word that conveys modality or negation), a target (an action associated with modality or negation), and a holder (an experiencer of modality). We describe how our MN lexicon was semi-automatically produced and we demonstrate that a structure-based MN tagger results in precision around 86% (depending on genre) for tagging of a standard LDC data set. We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations. Syntactic tags enriched with semantic annotations are assigned to parse trees in the target-language training texts through a process of tree grafting. Alth

Who reads Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT?

Author

Kathryn Baker; Michael Bloodgood; Bonnie J. Dorr; Chris Callison-Burch; Nathaniel W. Filardo; Christine Piatko; Lori Levin; Scott Miller

Publisher

MIT Press; Cambridge MA: MIT Press Journals; MIT Press - Journals (ISSN 0891-2017)

Published

2012

Language

EN

Field

Computer Science (Physical Sciences)