exploit this capability by including named entities, produced by a pre-existing tagger, in addition to the MN elements produced by the taggers described in this paper. The resulting system significantly outperformed a linguistically naïve baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English test set. This finding supports the hypothesis that both syntactic and semantic information can improve translation quality.
author:
- Kathryn Baker
- |
    Michael Bloodgood and\
    Bonnie J. Dorr
- 'Chris Callison-Burch and Nathaniel W. Filardo and Christine Piatko'
- Lori Levin
- Scott Miller
bibliography:
- 'paper.bib'
title: 'Use of Modality and Negation in Semantically-Informed Syntactic MT'
---

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Introduction
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This paper describes the resource- and system-building efforts of an eight-week Johns Hopkins Human Language Technology Center of Excellence [ *Summer Camp for Applied Language Exploration*]{} (SCALE-2009) on Semantically-Informed Machine Translation (SIMT) [@Baker:2010a; @Baker:2010d; @Baker:2010b; @Baker:2010c]. Specifically, we describe our modality/negation (MN) annotation scheme, a (publicly available) MN lexicon, and two automated MN taggers that were built using the lexicon and annotation scheme.

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 